Maths-
General
Easy
Question
Consider the variables x and y and the corresponding data
a) Draw a scatter plot for the data.
b) Is there a positive, negative or no correlation between x and y?
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)
Hint:
A scatter plot has a positive correlation, if variables increase and decrease together. On the other hand, if a variable increase and the other decrease, then the correlation is negative.
We are asked to graph the scatter plot and find whether the correlation is positive, negative or none.
The correct answer is:
Step 1 of 2:
The coordinate points corresponding to the table are:
(1,20),(2,16),(4,17),(5,16),(7,14),(8,13),(10,9),(12,10)
The scatter plot based on the data is:
![](data:image/png;base64,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)
Step 2 of 2:
Analyzing the graph, it is that when the variable x increases, the variable y decreases. That means they are inversely proportional.
Hence, the correlation among the variables x and y are negative.
You could also find the correlation using the trend line. Draw the trend line, find its slope. It the slope is positive, the scatter plot has a positive correlation or else it is negative.
Related Questions to study
General
Choose the negative adjective starting with ' r '
Choose the negative adjective starting with ' r '
GeneralGeneral
Maths-
Consider the variables x and y and the corresponding bivariate data .
a) Draw a scatter plot for the data.
b) Is there positive, negative or no correlation between x and y?
c) Fit a line of best fit by eye to the data on the scatter plot.
d) Use your line of best fit to estimate:
i) y when x=3.5
ii) y when x=0
iii) x when y=1.5
iv) x when y=5.5
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)
Consider the variables x and y and the corresponding bivariate data .
a) Draw a scatter plot for the data.
b) Is there positive, negative or no correlation between x and y?
c) Fit a line of best fit by eye to the data on the scatter plot.
d) Use your line of best fit to estimate:
i) y when x=3.5
ii) y when x=0
iii) x when y=1.5
iv) x when y=5.5
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)
Maths-General
Maths-
Find the solution of the system of equations.
![y equals negative x squared minus 2 x plus 9](data:image/png;base64,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)
Y = 3x+20
Find the solution of the system of equations.
![y equals negative x squared minus 2 x plus 9](data:image/png;base64,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)
Y = 3x+20
Maths-General
General
Solve the reading game.
Baby couldn't finish his HUMONGOUS sandwich
Solve the reading game.
Baby couldn't finish his HUMONGOUS sandwich
GeneralGeneral
Maths-
By completing scatter plots for each of the following data sets, describe the correlation between x and y as positive, negative or none.
![](data:image/png;base64,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)
By completing scatter plots for each of the following data sets, describe the correlation between x and y as positive, negative or none.
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DXb4sGRGfx007ig9Cc76TAWiraNRQCx5J96z8XRnpGLIptKivIJcQzB/uPa7N+O7+cfgvDsM4eL8N0nC7b/cNUKv6d3EM1xqU80z4zFTfwn9nrbFwKWdxe6RDAqo+1M5+yoWJkPmH396ymyHrmWfxnpMGz6relqCb129GaXikrKZzme2w+uScCM4ld/1HpGVnE2ThVsPtvw2jUfrgpdo+BaZ9p0dguUSV/rkcaUrN+m1kjO8LnLkQzThXe+M/u7a6NmfYZybm4wwtAMFJAHAarfvs6S2SJ4yQb6d2Y31eRW/KXX8DujyP7l/bnseViq7wt9rnxz1Myro8NhJGekdjOPJZnw2RsTZDlPwuPYp8hhUkRFxOndRZBPGD0C7N+20mv4t8tRPzjIv5vVMHYan9udT+l6uYrSimjUkhr2mfm5yyo5K60v49AY9ZviybA+EfFWLLv+M/T3esjeTTtrEAGGvnxC9RjsOI1ZnXepPb/K8vvZeK5ZTVOlW32IRXGZPn1VB8YjE7783oN/3kh2bM+Ngh2+zPLPvlDsVCGegys/mH59Ip7q3KsaWqCr0bkMCu/ZvdJGhKf7c+RMyleBvRBs/znH4XKdqIi9kepQmPZZ3AJfqOA5YiR/cKtKhr6nM0KWd10bKOEVb8tne5gNYKHsY0S3e+fo8Kq30Y+fzFrkdml0qtk8MOs+u02aptmtV7Afx73RZ9tn0XA+C2Yyw0uSSjY7c+0z18uYZEG52cKVvuz2P0qQ4LYWJPBMP3nJr3CHl4/9IpxXR2t76vEglT37B8GGaeJ9ClNWf7+MKzvq+CCg01YvKchXc0MRMGN8brILBFp9Yv0YR+nCmb7s6RskJuz//aoODP7byutAtllUY/M2d98/2z3D0OUFcg+mddVXow8eSis/ttIF0zGjev9Xo0UtuU/N/kCe+1vlyWJcSnEan8mj/CfQXtkc7nVf1ui0c+qFTOMcVIw259ZMaRqmNjvbAyi4OuOgBQhVPolt35Cw1zflwO+v7fHD7v75bE0Nb8a6+ja/jO4barz6ZyWb5wqWO3PKb8ddnL2cVCJezAs/3lZC9nZ5WhD1O2/3TwZ9jm69rO4zKF3URR6bAMZKnPz+2cWLfjZdp7Fp2PTnfV9VXpZ+GH47WyMY6DRaX/OK7an5Pz0Vx8dBKv9mWX31MVzxnNrtz+HmRumeViMTSATc/wwGLzo5Hvs1ScRNe6kwDPCacHunx+FaZ9ZG5/8nC+r4mJkPbQ1PkkLbtO6CFCwZ+lqFMz2ZzjpWRmeln7KnudjYPXfjmYxyNazEoWekQlib//tvC6mbsBOK07qu+50Woadq/tibQ2vAuVgabN04T2PE4vl3KQ/kyS7DWqQHmvxrPZnJ0pnxbQoR38cb3//DLuBchsoQgjjuHWNXh2N086LoizP/luOdA+6/cPy8AzaEOaQx8rJywILTcyHNcL6/hkchreFH5SjO71a44etl6f/BTV8XGrCOOFaKlYVXuI+gDDkMTLOTPtcuUlyXtTSB3Mc2W77c/NkfF+lMfohiPTsapdAIApqsZTUBuYPG9jje+ZhGPI7cxa3x3HcbX9WGJvVW/4ze9ulKPAoaYyC1f5M0Vanv1kCH8du13/e7KDAgr0o/T0oNdv2WUj9CE8HkjIA02lUfC3m9bKlLGCpR0aaWb8NPz8M78Co8D0Gdv9tZzIPYTYpg5HcvtP+vDugeGZyHuuEWO3PrDUnTQR3WjIMdvszAYcJ9Mfy2+kfpqgJXTk0EFb/bcgA7y4FlbEZhTU+ybbIMzYbtr+v2hR5SHVToKCsP8655T+TFoW7Yu8GdWgYkJ53USb1ixQEuR4pXLv9eacII8na45PIlu0cY8l203Onfhv5sGwRfwisuomnN6JfZv9tsk0XZ3x2ZNZvk01nRYlgGaUbb/xn6oQIYhzZbv9tSuETyJr+s8p8qRokrA8OQ6f9WVIHP7FTjStqGZIf299XMaqoCGPZtb6vWlEN1Erfbgys76u0FEAYctbHhiFyvZ0UWJ7E+/M3QJ4W3vjP5vcYn4ipWb/9eXjD/ifhj1He/jxEVnr+PFj9wz4NrdV/+/Ng129/Gjrjh30azPG3PxH2eAafhu74Yc3PcxmYVw/PSyz0P8Ewj3/s10QL+M8IEjwEKKrD6G1/DQpD/MOe2mx51YQHrTBtiZsiqI8Y2NIfgCZawn/WNN+Q3R7++NpOM3Crli09vZVjwzZ5kSx2e7tXN2VghvdBEwGvEfznar05uEEjhOSqYWuzuPRyvSc/gZDdkB7ya6PoTFSMP/6pF55sQ8MASuf3KaSgKW8wnKaw10R2hwwiiF3XfZIflqdzBhk6d59+IXT7hAMf/QHe6/Tp4onAIVLD34b2u/f0+V24ySNCXLmrAT6f3Ft1+GPr5o/sgsKFPqDe/pz83mJ/KEjW2xIVwkIWAZDVRwesbuEpKRB4jAh3sx2+eXKTmNzqQ4INaRyhlLHHV+gBg8jF/ZepEiM3QoeKBairhq0uuBW2sHKX/5qsPGzQj3e6iYcN92UF+MobsgOBO79CCiSjHiWpS3b7yfMNcKfwdtMpsLZFfPPVu/E8+QluvEcdUpAT202fHzZxzJBaBep/s9c9rv73/G4k4L3Gm13iUf1pWDsHQSJ48a8gjF/84t2rg13wur+uezbyF1OSCH9VtHoCt2LDexgyA5pU/EUd2ULtWIc+jBvvhcLdYkvNJMswfsb61w1YJcdfE5C9h4h5aAdr5+PA7VAFEhGBbKHJbqi/88/gOz+18sd5XRkUbAMCddF76/4N10dIQUWhAbWzveZDqxD7Gne9g4fpMgcWXJdqOZE/vapz8tsGDv5O8iypEFKrkPvbcrIL2jTsn1rzhyzdHc4Vo8Kn3ry3vA9F8ATbEwSyabUlu71PVvWMLZcmu+pyRcgKbdbUTTY7+WR3I5e/bxQh+W1v3gVkM/1TbQ/xnj6LYLKcCIH3QidpkfAyfXxpvKla1GrLIl+SBfygPFZIrfidcGCzzY6moe7dtxwAKC0DT2uYQG7S94K6Dm6WzdP0DX/5qbXI+Hge2hBUC1RFERZidkBdvnfjeogzBOSAMIlVU35vxbK99/0Afif55Zv5q/7NfLHb/mEfrW3k9e/8+AdcbiqY9P5w2ASy28H1jIo5ktuG9P6/rw+Tp3wW1nq+2E8lCjwYXRE+EaU3qPaOb/feb4vg31QQu2Z38w9gy9/bANGtIG7N+TFMsHVwe9eH0byd/1kes3vWsPRjjTfEtgT+kU02jQ4jKbgyv69SkEYgtjyStjygEzgMu3+YiRGsAm/rt4XeOKJOFNzrUAeKLkQNgcjOB0QAFEb9NqJquFJZWNn9tzVhcEUeN03bQyTy5pMfBWkvZ4Mjj7EZywj0geuZtfw7dtX+5km7bT+qznOn/3Z0YXaH4eRba05BtpYQFn3mo4jM+Z81TbWqtke2Pso/H6X+9cX7ERhfd4LEWvjVpFtNyl52b7cPZv8wSoFNjtzsWfTV+2C1PwtZ/DbsRi0rfyEY/G33Noq4H2Z6VjQUya1wu0sv4VrzP2uyShQUrPrXz9GhfpEWWP3DlAS44m03/HWXHois+Z+VcE3OFCFyjoUn8d9LF8z5q6hSJK1IKZ0Q6lZAX7wXjWvM5867QBV/sm7jL2LfBzKrFn31Xuztvy25DdgDMYbYfUWf+Sgiyz6rrA1ZHOghLOOSySOY3zUsCqjnHoZln3EbSHJwZLw8O1aQhJA1ln49x+z5n0lLbiXXJMJDim8V4qKv3oeJOd4QRYDbKV1NHgc1fbUrzPbh1vr+GVySG1JSxLhoOWx47SVcu3+YJihiVYIQInyaeoQ+cBimfQZNiW3eL0QR3HG9W3qg830VGNVRpxhEiO8g5ChTBjqq/z7s75+FmiIvclDHlGzkgfKMHrDHJyENEuUq5CgF2RGy+oIe6H7/bH1fRbrkl7SFS3VgAJrMSM+UBEQaRVoqJCp5qX4VQS/urf7bzMzwFyFjE7oILyaQkhDeFL7l8GFY8z/jbmyZT5Is9HYxURM6yInNNTq0D7Z9lptJdvMEfu2pqG6I9eTWmL+KQhDh8l+9OH6LiB9xIbS5SP3vh9V/W98MslQ87iG0mGgzpa6B+qnAfpj9t8Eu6W6EiyPRAtyrvQ/FmT2+J7ki2a0UIBTIV4Wdda4krS/eD2t8EmgW7qSKMfnhAONsI4N1jrTEJ6iL96I1xg+TXrPgSnHL/srOAuVI2SEtirkvu+/Y5x07UeWXZVlXCxxaMBymfXh9B3b/bWpwWp9diyazn1tVljnzd75YzmeW/ISlB7r+M8iWlRbEOvXL735FAUEWeV3+V5b+ST+xmOOTgJiTh2XN8Uko35NvpQ+yYo+aKCS3/nUvqgtdwyRAtDV5Vc7w0vKEWZHJB0u0+k5Uk+xzz8nUrP7b0Ljo2gclMqhMcRr4Mni440iEliE/aD+Ixvz+mS8bXc8QTaCDMMWYFqWM2eK0G7L62v0IrnYDm4kmp+FZSDUA41V54RazJQ0IuF2KFGb95siyvq9i9C9/l5tYEqGERSiBhtz6KQK7hLoHXfuMOIM0qVMN4sy9KGXWXJTZKh+A2JnkD8OwzzAO0I25T7IS+WHhugEiiWZpnVPdGGO46DD+647vafb3jKrgJvZK5GZY5m5c9Exlb2H7z5yINvC+hBQPWE5nLseVYxYKrsuXGAh6cW9/X4V3B1lO28Tscn1dPIThmRuKOWr4/Xbce0iG7NZkF3nZHxkxiyajKm5/h5cFyWIvTEg26/cdcMc+R9fPfzhtJYWQlxeFi9cGIQpFkZ3e9XPxTP8ZapbOnmKOH8Lskdq1kOG+RBcfSNb71qtPs5X7IFGc1E+PfFEVaRzmO57mUqTNn4RszxG0C7OohsJOGCTxs7DXcC5WEFIzIjhN/YV0i7wPt9b4YXj1xbz8yhn5RAgQA4fA8/EIpBY3mIfFA99EX70X1vzPakiAC7w2OF8WHtnN5iS7Dm+D8DQorvFEffU+mA4o3jy6/pa93Eppx2lOH4pbL85Cpu11+uDW8/LitB/Z9+Z/3h1o4UPJdLEccWgSjBgh2a7f5gABs/tHzkjCpLecB/EvyZPxAmlR6HxOX7wXVvGidRZpifRM8Tbtj7OrOYwpp00F2bwMfJhr+Za7B8z5JZG1p7+zOFCzSsu8UM6SA8KBbFsW/vN3ZJ+9yHb8Z1gQkI20Pa6iie9B5ajJEz/4DiGEtKr68n2wx99ep1XwivRMPVa0b5ieseuExSWk0HPmjMaevwqG6ZLTYZAmaTErK5bUwDYsSkihxo6+eC+s8T2bdnLnI6VRlZ1lcFtXYRHHYveRYSDOWBTqQ3Zt+s/gLrqub15qGSsClKkAsedHSMKpe45MfZ4FHOaoB+zvq9bpqYwZCpqRPy3nVZnEvzgoZ+pyqpAqwbleuaVVv71ep/OL+CdeAHTT59NFVGUyACeyKc7kuSiynuOJvum/bX7/jEyIdJec99apLkYM8GSO7ynK4OS/OPo2oopshq8cX43hiDNnCPrphtGYgJSwcvKM9lm0+FHGz7pkJg1NLnUC7ZVN2N8/g6Gm3Mz/XH9lhgdrQD7XYaHzODzxMDrjk+AWkJVC1UJKPnmhzLVzzfFs+sO0z8JImFzJ0BwUdVM9uY8PYB4pMuAoQbyojxRs/5mRpsbfxhOY51Qwq0p583Iz/mIv4drfPyNR5e4X6Czyt/klz+QXcYKCybqpN4W0Xtxa81cxC4ieJGuHDCiF+inhPJYtYo5CwMv1VGfTAWX2iDijYjXOshQK/z3GMypEHKwYwz1LKa05wIdkWPXrhZqRuOLg9I4f/6JwK+X5+V7ZS7YH6rdBgxMNsGZ/Ul5CxTsX94Y9fhjt3NJ95exVeARozj1aGCp26n5E5Tr+M8IpBEpLj7TBAUlX4N+F4GDwVAJFQpf/AzDHD5ObytczShuBx4CHkGKQv8qAjHIFFPwwYNt1iGAk+o8BPaO2BVdQtiAukViQ858J2RX0p1deb6ZnkecV7DO1WFJbWXNoQFCq4XwIelGNrPE9qcky/rayz9CH8r9HiBZPqW6ZqQnj6tr9sMcnASlkY8/8j1LQAWf1jUy0kBaXQk8S0WF06rdBrvBmYEoKPE51+S14hH1mho9yYctU2YtbpzDGD2POwDtZLEF5RU6kUyaQ1H0VxS09rRGHYNZvi+BOPRelEjwikoa0SsbWV+NEr2D3ZQDGwyi745OY80vS8dQDe0a1e9cOHw7PHD+MVbDQbcgVxyXMd5EiDPi/v/App57txHb7M8RxMoV9piZfJ8wpN5IIk6zk4Gc9i9tOYozvyeoOZr/gilM3wZZAw+Ye8vd2R7aXzi3ODftM3YieYZ+l1VVKhTIyKYLXWSJkUSzqJXBjfBIWIdoIfOGlIVs8og5ypGdOT5M/eUXNAXdF1AcxsWrvxAXd2GckFr9Iv4l9htVPihnI9hWuNX4YE+uJG3OGcTxCSUJFWeR7mV+JDelDtlu/zUKqZO0i6DwokcEjxTWhl6TOag2LR1n3wIPlP4NhNRAy2EW6wyORG5UrB2RDaFtUvxhjP+0BDJxhn5mMJZJYJ8P8EklCJvTKM87XAy3OPJmf/iDe2GdzfkkKmPUI4D+9KPnxeq+M/R1Y9dtMHyiZiP9MuSC5afvcLgIvjm9KzpLUSzesykdQ3dhnkmRgxezzm9N8e2XFyhzpox9d0z5T5VBKg78FOVfIJuQ5GScwnLHa6gLes0TBQVjjk8gb+hw2FPdK0nPuONQ54hZlolgmoOppmqz6bXI3Z4qgkqDoXoStqErjzN3HOE5mSIO9yFrje5JFGX8bdKBzyOZPnTp2lzAlefYFZP1cLukB8/tneW8UsCFLYVeyNU6TheyEquAFvQdte2OfJ8XjTGIGWRFyH5hrn/lQnN3xbJj0nG3J8p+FlmRjZJyrM3mit1CzBhIXVt5N1SuN2P4z9P2UBpnMUgoo/sDlatfpVVyzMSJ1Y1VYOYTvnf5hq1uzuyoTHCTwtc7L4odIX5/4KKz5MYRMmnnwE3RKCD34zwis8lM/mL7ELudo01fvRXBuCJvc5to+I3f3KubDSM/fnSj0y1svvqqp1n3QHT+MxWzc2ah4w7PS5BWlrApk7+OEIxCqa/ej4z+LbGH2IQJmyovqobimSNhMWHhIIj3JWuVt5rooNyj/GWIOyiaqHx/o8aV1WWScWrBn+7M9vifuUfNLMsbyEsyJYjfrZV0GWfzljC1A+uq9eGOfc5ceKIpqFARkPUv+Q6zmoR8gA5r2nJoV9tlokuAttM9iF9ZOGIR0wGCfo4eY/tfaqZkx90F3fgw9YjgpUxJtlbmpA6MtcdBUXr9sAv6zEWdgUtlnBGmeo7TMpC4PVgnkIhZh+k0H1B1vaG1NvyVyyYM4C4K56kKhz3wUnfmfqREcTV+Yh82nfWb+TPLr1Pc452Av3bCKF2Q2dR/ZXuU0vncTgmPO0F/KyTzMYpnku8872O3PSHjwY5gp6/QMAWex1LBFP8JfyCfk2Qcxcc35q5iqmJ7JLX756RRGuRINpza7sScjlaqr96LbP8wJlf/MtMGyoVIVUoruAu8FmtGHqjW+J9hiUY3zY7DkXQfIeWZin4VsCmNKAamL98OaH0PeHPYZXjkUgSvKBUwg1MJVigJQVvXL2rv9w2Cf6T9TnBPmPovikfa5YJslzs4TNtz0QKd+Gw/S1frcwXqaMDP6zjZL7N15L/2a4Tv9w+gIbO1zVEGcfzjjb62zh/xWKuEO4016NueXlNRG5xO2ggPXSaQOgjk/Bmk2znIq/jNIgmao/Gd4D2I6Zh4z7D6wOsuT8MZ/jvwXlfBYXYE3kSqGPzQou+xqD6z5MSRTfpT6bSRA1Wp3d6WqCbHeTeOnyGDj73hTv428/lJSM5Q5rYs/amJhWlgONB8/Gcl0H6Yo3WgofVD+M6iEAf7b+qUQX4/xNymRI0FAh2GNH4a76djCOSSZio6B4z+y4En7B0GffeldYSzz8SjQBnF6ePGfRQxQbPdUSQdCWIV9p+GCR2Rcx9uRgGvSdFqOU8/JeaBUC6RnFmRhn1lM7gG7f5jEGZIWSn46G+aEEzzIOFiLfd5d/3eY81fxveF5exfMZxlehJdTlM+uEXMsZUL2nFivlxi6/nNrVs+TX77BJY2S0rteevsWdv02eab/TPMD8sgspXKC5PFieKfv8cVu+MB96MwvSfus/GeIx5tT0U4kPaMQK1lg32Y8a34MShv+s8x4/JuiANPp1StnmWYFDtQcqrxT/b9jYfrPkjn68UOktBgE1mkRo1AhYgCXUi3Uh6xtn5lWxX8GTRSLJ00EWSDhsZ2bZFl/06u2sTu+J+u3WakPskG5BD0f9pnxR9nC8L3ACPaha7U/M8bBkS8yYMQ5J2cQrEhEyPoo4/fi1rbP4FzZZ5j8qvCbyFkW8JCa6Ez6FEX0nz9un0UKPvuTMI7Ie/R8Rr95jXyeVf9wSajWPWCN7wlyrdhnkmzaVdNOZvdfUI5S5W0IxY0fdh7gHnTbn6ML15jBRGkAMx+UUbAz2D43mVEOgLeIBCH2WR6AMovyn5XG4UBozQe3B3b7M7VhU79de4l0HqkyTkhKslCcn5YQ96AzvidFwPmrKOCEzYPrKqGy8AEg23fuJbt/GHl6jgMkbFENvkiVwYbwBAlHhTyqBzr9w5BxJaz0cZpZUvjlc+nGf9hdVZ7IFMKq+sOwx98Gh1DWKfxn5JV/iu9l+ezGySVr1xCbIFh75S4u9sEe3xMqBfuMpEURMJ8PLtn7Sp7HIdpV5dhhWON7gqqUt+k/I7u4Kn0syYtbhohI1Udw9tpPutb8kuCKqkAfhrKkKApGfdsgPZ/gWVH4yJz0MKzxPUXDkOnSLacQWrw3ylE/+faPcz6n0l7jQextfxZpYxfOvmT3SumGwJofg8lW/GeYCWagLG9r/5m7UKKTX8gC+8D2n8ErzL5qf75GeUqSSHJPieAI9sqswmPU1XvRGX9b5n8W9zCdopgGucwTsM9TjIpLpOc+krG+f5YXlfowlZHJvrSRMkah2Y6fQVf60DVnL5VbxT5Dlr7nfZXhk+JXeo0iBkikX2daq/1ZGtjTe+U/+8mLl3y9eY1jKfBQBqx4RMETFx3EW/+Zc0uKUjRt9A2mH68geQ9LRnkhc8odhmWfZRLIB9ZvN+v8Qkbx8V7jl5sgD1+Rp+OpNQ1qH5j2WdKwqt9WKnVdhJAR1vBeymht/aUnWcN/lpp9ttnquY9QLsEjq+Q8ciawRnx8lanm7YPw37Q/G/3DhGk4zso+81l94us9mP4zaTL6Vc0dI3TrP+PNJF7v3LKfn2vNX0XuQJb9w2gLPWkE+faqKhIYji77+s/meL3kUM//jEAg1ShsZ2R8gGce61mayM3+ntRY1m9LNkkbDUpOKZaeMkASKVnd3QeG/yzconzDbx7WUVqHdRjWgZfVKKyoCSOcOjvtle6iB9M+k660PyPK0vA3CRdxJn0F5AWcyu3nj3b6h+FO2GfxkRBc1PRiUBCCTuMQJJK6/RxHq34bpNZin0l2UZ2ehqeh/+uVM8qkV7TLa+h00E+ZzfG3yS2ydhpSKmtTSRexVQQH95dUly8uWc/SA63hPwu3KFBeQOf4AG6Yni+pWo9STQOD1zNX22efhXTDqYRhn8V0Dm1/hv+c7m4lUfrPOCKZ+65/GGKQCbQupDHvMCznhilEdbgDGZRfpKdOiWSNLIliguJIhq2u3guzvM/UpeZ/5q2hx0814K3RGpFbZE1nUlt4GItbo36b3G7ss1BGpEY0RvyOnTzmRfkDV6mr98Jqf8arq/5hdOcQhr1X/jNDuGDi/0TeqS7ei+78Vbp/GPhkRIGW/yK5sHQFxatwcro+6mG2P4MKi2rS/gwyUX2ZIrRahJx8i+ec0A155jBs/xnvrvuHsWhBbsXBX0OZ7/Ew7LH5Q1+8F+b8khJnz9p/dpq7gH0EnKi6RubLDB8lMDZK9MHUdP1ILPRAVswZ3pb7WYiA7u/5raePdOD7KgoZmealKm/jYX0E+x7M/mFUDSSCzAs5iwEME30veIASnzL7XRpIUbkHzO+rSIxkaZ8hIDirMG9pRl0PgxleMy8Do95oL962P8sXX3iHVLzapbis4BNpe/H97Ac7Ch3GZGqVt0FPyFLdoohZ2SyDRXGqwF+A/zLg/GT66r2w/Wf8whu2P4OuOoSsDfwt+BFlsz49Y97cA535MUAM9pmldiYzQiqGnPw5SHE2vAhX/eha7c8ULtOzMD7xk+LZL8vLaZCzzRhyTwN/+7y9sOaXFAVY3PJ7DPYRFKBUP+OZNKP7G171a88Fu0YXREpB+8/gqvrlXvJD0cJF6qiy6ZLV8SyL98jVzP5hiHiqvgdLymYEjqRNPWOBkJWSKALk0nGwD/Z+X0X+nSgPp/FrmcqXr70i7B3Y80tCfZe/kzi4pm1C/pyWsTfL2eYTeElQz+Tr516Psuq3IZSTmmQlb3Cq26CqgilLRLX3WPgzTvNJdewBc3wS6IbMFxvmNH0oVxZVVbL3FtswSfaZleZ9yCJadAhYr5pFVcTTMEe2mf7MglntF/4PZ9WGyZdbv/ZL6RLSx+KZ/jPM0Y+0fHl9XlK4EmXrWhovodBZCbL0+/twa9dvQ7ipH9/7Kf17mGSc8tn0w9KykO3bWA77DPXXYGaWI86KioWeWRJ78eOXx5j2Gr5/oVShF7coQZnlbafJw6u4uEPGCIbZDLGAl87kva6z8jos4Mv0UwXTPlNZr4v4qmacVVn8iOU1jm8h/vb3tLyriotKSoKHYfffbhZpECdCdl3++en/9oPLtGWpD/5GmJauqhQ6jLffV1njk9D+L+fffD+8w3VSFBgEc35JySSWle//VrqxXtzV/ixcIl9rkP5uy5l04NZX74dZv01P9iQkWZSMGXH8wPz7NQItBHIe1HPJJXpJ26rfdpqTaubPOP8z7CV7b/nfK5Bpct/Ngm8ki7fQl++DNb9ks17lQpafGS7BYFGKk7tuQXYa+KpfeC902p/z9DcijAVXsI7cqEln/AQhOuWICeEdg/rqveiMT9JEKYRbQbisfaYoUp81mlFIsohLqGKv4kRhjO8JdkUVTtlRexKWiLH/sM5QjkqDqXtZz6VVoQfs9mdYuRxk6+uFNBQz5U5Cn+WIdbsIYVTZ4byXJljzPzct8mAo63wp2Zv/DH6fvz9X6xVyjPlzWcJBh6j01fvQGT8smpBstWR5tXazizMd/VDpFNx+Rx7cj9s3EzRa31cxGhGRMCUsA4DRof5zp/82XoAFTDU0UNuCOGtAKIgoPzkBAzyuL94Le/ww4RXEhFWciFJoH8UCsmk6YWKhJ90DnfmfwSG4pf/JllsQ4yf2fEaepxMe6acc9ni960bIylg7wmDOPASPINklngBu8Ubq4r2w259BOAJpUBVnDPvraAVC7To/SfVIO32E0P2+CnEGyjIojvAFPtm84rQTRZZ+qr54L0z7LJQQX6JeSLiQtAhF5MA4o8/ai671/TMcQ+EWpCkD4bZllaPEEwowwm8/62T331bFYRAERRkbkGq8goxBdpKnEX2OXlKw+4exBlAlAdD9kZ6gcKViEnQhB9pSqvFhdL+val2jfpvURZby4kouw8D5JbdZAZgUgvjTwhYw2lSwEcdaggcQZEacbCUpcajdmBXr5Te0+uZyDt2gDcCuvl80Qo/QB93bEEM+0U83Ov3DJIbg3Rn6Kt+b6T31Eiq8H2b9Nq2yuknu1zoBEWwewn19xX5MzPptlTFIkLmlLAjvyPKCfnFmjU+ypWrzBA3QIUPOe9Gp397eIxzKOyuJsCVLjqsXOAyrf5i+WYljBzogap9SMF/kbzD9Z7Ko7mFgI0SJyO2ZnprwXv22wSglISwiwxOR9CP6FrDPRhWsUgjSxkasEIP44R1gSVaSD/Z6lN3+LIT5A01u+H0VyAhdnMcRvoe6eD+s75+ZFa+QB1MCZJwsC998IBMkT/RRZWt8EtJtObOpVjgIgQEQVS8vwumncq75jZ7wt2GTkuUD1EGyrKj2odvpHya3kRJ55K5iGjRxFXflih6w5pfkTWRQqa+YeIiS5CQZM8ZEGofRqd9WvArPssrrCzmoGJ7AQD92Tfu8ZRKEND1hUL+9PId8H4b1fZViUPHK999Ik89qkR7kHXqR7X5fFZn+8+ehmXbnjf8cBIZz85nIzFrBT8Pfx9MfB9cYHenz0FijL3wezPrtzwPK2/9Ew5yzf5IgBs+PcQDd7zGcwguKs6B4KDa/3aL2go+vZ0HwJcbtGwL8qYVnN0SLh80j5dB272+/syLgkDdbMoqUeYVa9UGE1Z961rs/kOQSBPFjEZzJrpzYnN/89KL+ZIO7sJHQ3zaQqvcowlV0zRWXyNZczAeqs2+3pHR2+RrfbojiSXyY/lMHjEVdgNBGan9fgyKJHwL+b1YuShY7WlzUEdnZxvG7W756UDLO5BGa1uaB+iL+eHgXUOdV4C9bUCZZ7Mg+N5qsvkQO6wU7er8vXbW7O4EDDzr25RA21l3Gpe9tcT54pBS2jMphtaiwdb0hkH0/kr3q+M/OzJ1Op5lrLjgg+MWNcbz3Anq3rjs91zeTiBAEhKaAoc0NPZfp9PbnOQIbuvqwLOeKqoWDLzCdnp+DzalLunKDefbdhSTlysPcT8/dgn9yuRwwsLsMiz52mAEwDOleQLjmUR4390DP2BHRvLnkvUXxsdmVHQsq8jbn1bKJ5PcWXo1Y6rCDgzstMLA7aF79/gINcy+gCr0uBpPvcf7egmvc2/evFAoKO07lFP73xxtibUpVwIXbg3L7Dpqk/pPz20v/vuAidpYx0Uw4WTWnrFbLm0mmNyf6LwBokK4ivfvvgsfNcyaV7mJerfYF24N/nxt7R8NeCLkLwQ0J4+zm6G4D8PrNkxSNzjW7DaEu3dxAbMPba+zzckJR3lxjbjfY8ri7dHeZSQ/73OVhdfHbRa8bbA//BercZqtvVZcbWxXUkIv3U7Uhl+vAu1v1v9Ex/ZNVP4aHNBBUEtHnddDeqiD/5Vrsqku3xOxIsrE5p4i83RI7clz2USPUtfri9xecBf6N03HEEUccccQRRxxxxBFHHHHEEUccccQRRxxxxBFHHHHEEUccccT/RzjO/wDKiiiUgr8tRwAAAABJRU5ErkJggg==)
Maths-General
Maths-
Find the solution of the system of equations.
![y equals 7 x squared plus 12](data:image/png;base64,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)
![y equals 14 x plus 5](data:image/png;base64,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)
Find the solution of the system of equations.
![y equals 7 x squared plus 12](data:image/png;base64,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)
![y equals 14 x plus 5](data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAFQAAAAPCAYAAAB6Iuj1AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAABGJhU0UAAAAMyZLetQAAAZhJREFUeNpjYBgYoAbEtUB8gUj1PkD8n2HgwX88eEDBYiBOI9IhPEB8bRAF6KAGxDhwJhAn09gz/6kdoHOBOByLeAEQtw6gR6yBeC8BtaDA7sAi3gyVG5AAjQfiLjQxLiC+BcTCJJYl/6nkQDYgvgTE8kSoPQfE4kj8RCCeMpAp1AuIV6GJ1UMrjoHK8qBUl0OkWg8g7oOyXZBSNS0C9BcQfwLibUBcBC3jMQAoFV5B4osC8V0g5higANUD4qMkeno3ENtDWw7CFNTWxOQwFiA2hia4G0CsgU3RNyR2H7T8pJWDCAXSSSBWITFAC6CpR4/OtbcbEB/EJnEYiJWgGJQ6mQawlic1skCV1xpoQogcgObQD2yCC4HYD4iXAnHsIGzX4VILSgCboJUoqDw7Q0SWp2aA6kATINYsMxdaszIMkQAVhVYOwmi9qsU0csc6ILaE5l4maGUICswgbIoDoAb7DVAXjlRPs0E9KI1F7XKoZ6kNQqFNyT9A/A7aMjLFpRgUkMcZRgHVwDZoch4FVACggnXLaDBQDgBPnowtST1g3wAAAHx0RVh0TWF0aE1MADxtYXRoIHhtbG5zPSJodHRwOi8vd3d3LnczLm9yZy8xOTk4L01hdGgvTWF0aE1MIj48bWk+eTwvbWk+PG1vPj08L21vPjxtbj4xNDwvbW4+PG1pPng8L21pPjxtbz4rPC9tbz48bW4+NTwvbW4+PC9tYXRoPhzkFMkAAAAASUVORK5CYII=)
Maths-General
Maths-
By completing scatter plots for each of the following data sets, describe the correlation between x and y as positive, negative or none.
![](data:image/png;base64,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)
By completing scatter plots for each of the following data sets, describe the correlation between x and y as positive, negative or none.
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)
Maths-General
Maths-
Which number is next in the sequence? 35, 85, 135, 185, __
Which number is next in the sequence? 35, 85, 135, 185, __
Maths-General
Maths-
Consider this simple bivariate data set
a)Draw a scatter plot for the data
b)Describe the correlation between x and y as positive or negative.
c)Describe the correlation between x and y as strong or weak.
d)Identify any outliers.
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)
Consider this simple bivariate data set
a)Draw a scatter plot for the data
b)Describe the correlation between x and y as positive or negative.
c)Describe the correlation between x and y as strong or weak.
d)Identify any outliers.
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KnEH8UeXnxGMDfhvufjxEOiaFdvnDGPx/k5eResJ96k4VUf6Z+G/H0lyCeF7HS1+XPiJcxLcQd+Xm1tw+DXCpqpdYaoXYi/qTfxitevqvyjBcpIawNrqds/ozrjrz0yW6CrN3O6LvBiXkJYa7vu5AmngGHP9dM9TJEgq3fxnVGmoWKkW9dReuWBeUu2z4M11uSUm1ghZOOlElI9Av7bUM6FiQbOvsA1zqteEufsiD6pn3Y2axr8qSmVRjRPiz+jJOcxSMOsORnVH+5Jc8q/vG65HXCwjpjLPuws3mPP9f5vCy7BfwAJZtlWSh3EOz+54RX5XLTG8P9ulRWE55o30z6tJpnGbuOliKO/BxjkEEP9vwkNWflS0ZV/Kyyeca6mxD+BuXZ4M81ZZDEVPop8m6xzG6C0tjSdonwA31BkpZpU93pT/GFKT/D/WbZZSyPV+LM/ueErlXgo8Eqz3jRVeUvPaPBj4xB+gYVZ9ueZPT5w5z+57hw5g/DN5xGrF9d/tzGrL0H9Nt9fCQ8CSnNQP/Ftg+LDNs+7AgoHDQw9O74KjmlSswCZ/Dnmr7Gji3HPqwHdJbQ0ClDOps/B3SGfAKu/BwXVv9zdAzZb0fEgH47LobmD4sKxz4sLpzxz7ExoN+OCmv8c3RY83sO2W/HhKvfjgtLfo6OIfvtmHD123Hh2m9HRjxN8yAc/hwbQ/3PMfHfz++pHePAHf8cF+1baIfLn3GzV0aOBPT8p/Z2OOz5SaJj5Pk98fjrY4zLnwfm94yKTA263APkZyx7gkVnsLkB4BIE0dsJ5VnfwT34BdvBR+FniB+aPwuamhRsZx+9+8D9bAHV9wdsvUDvrzFwhtnjD+GP2FTkwAfIy3EPAS6aWjsVjsSPHqo3J0FGh/ir0G/j3iZJS+pyC4H4v+DPwnEgLw+EPuC19s7/IggL/iwyzAfcAlF4vt+gnsXiVcjpQ+jDgNjA3vNKeO63naG1JT+ztGLzisE2n1fCcTzUfcAmCKSXvQdy2xMX3kr3/Jd6fupF/LskqXxyODRleYlwrsjF3qk3iCblUj8KPRpyDK64F9svcbff5QtxUg9Ou1Qsfa/mTG3idi4gLsefyJ95bYJKCvmn/0yQ3XshL71Xp23y0+dpA26d3rDBI0n2EOr91eNORDQrSPML7uRL8FE61Sa/RF29TxVryIvyDp6os3ol4kVc9BO/Df7YvBjlGTMiplSVeNTXqCDkMk1hN/F4GcuzY/BNPEo/A846iOR3HgRDkH98dD5AzAY8CB1jJ17GC7/CIH0dgFAIMiMHf9CDRznNcCQo+ipjjQA96PIAViYH1IeTvu7d3ljRptLOPoBuj/TK8yLA0wN98RSOWr1V9EPO8sJJtncesH8NHgm/PK9ARRBiaQtn+VBc5K7cGso39YdTL3Aqm3u460E+3UP8MgD1atVUMvw2VBCCyMs/g7wmbyTEk/4hcXBIfPpO3kBMdCqBe3Gif6d/LS4qyjxcq2TVpRxc+sE+mdWPJA4OT3w1x1eN3f9cF6ETSP4ZY68X+/M8pnXnALpL7RgJL43UhIyG3/c/x0EZMvnlJzD2erHf3yMajw7AnZ9EO8ZBMvb6zyOvFzs0P0lM4JHXl4TybNlvR8ZFFWVc7e+A3fUx4mJs/bYzXjIyrPHPwJ+jLe01hLH7n//19SXt9TFiY/z1JU377dgYfX3JsfufRy/PTv/zqO2xf94+LB23+rbtt2PjP+h//rfL89j8+Xp0/myUZ8M+DFMulk+UoyXA/YV/CW77D/HnOo82m5u9ftVZfdWx3I7BWnwK/+lTcbVW9V0/sS63YuUjEbHmF1fu/GEQ8XbsSPq89mlIuetj4EX+TTv3qOWqmX75zrYPq/Musy0cE7km5099dxJ+NBvtUsC8Y1lMiXRjGgwB/W5tLIAaCHs8BhX0o2V/oG/pR/r8GVN2m6ZqUBXmy/fyKTjm6oHxGFdiAY5IWJjzeyaMAEqzRMy6HdmRw6p+J+HGLM/3laC/NHVkSSXpe+UCe/wzXnS3tsVVzST9V5/6yB7/jNusubVjv0uB/m7rlcus+Unajm26XyYpDOEnu9RrUvRZadTX9WbZEHJBfUgN40kvv6hQZymkbxNvfJ41PwneQPBJGnFEpmsfpl0SNyUhOrNidhFhWvoB/tyWYUOeDbwaM5HUXZnlWWPNtkqX7CvzHNW6eO+X54QtN1ddY03d8qUC+t21V3Gw+XPNj+tL7vFFhL/zC789vkrMiG3OHwIJAvTvvq790sTkz0klqurrC6MSpBXr2FfP8BvtbZx3NOFV0IISFsz1JbOy22YliShkmfw5L++2OfCEeA1U2z7Mkp8XzX46oDgDfRPHfhtfFxHTwxz/TMVgUtQ2Rif7t26DEfKMQHP8s1xzE9HUmM41yaA5gD09cOVnbtd2dcaRd/jt+bcRxI41/S9eZz+Avmfdbdhv4+w9g1ZKUha9xjXuOIbo8aM/M+y3a9nwok1EHZYhP/+UK6skVcT5AJ/65bmGqIAzizV7LsCSn+3+Z/RM9HTf284Ws3yQNJY+DG277j0ef14Y46u4bFugnPSZ0tXcS3JTMORnTGUy4+64xDGAh0yA9vv5PQ+gISvGDMwfZo+4okETZBjrS65KmXvx/Hx7LL40ZIIVo7/qYyapJmW02XqhPPQTmKqGPN/FK2/G/NuJkpzbxzTafEl/029D5STjv51HEVKc/ufFHeW7KlqfqMmfZ+rb2sdeJp6xYus/BYc5/hlLOoif96aMr6sLnnjTd8c/H9aX1Jh1Zd6bz+VEuPOHGfN7AnDW5AEToBvrS7ap9A1lvUWMatbk/vE/pN9Olr+sOioABn+uVThXZbT1nszxzzp/JlW8CUSd+cPs/uerd5Kjs9V+XsBA2PJz3eUocIlYA4P9z+1jr5KC1tmuWftOkTnU/4y278f2NrraEdJ4q1yH1pc0e3ShdU/8ww/tbaus9uf3FGgLct4w72ky+vIzzokaHZD1JKqw8D8MjJekLOIUAUP9z8kynsJ2qP+5XgasYGTBmT/M7n+G1mSzQFeR5ji0+5/zdQ0SYkT5eaj/mTa96u+VVcU5aZxOoM/BnX8bYujp8kgf8WpZEFIuvArEwPwk+/Ux9mirZQnh9+zjcNevAv5scOwbtixTkvppt035GXX7JZneDg3WDwr0z0nhqVvFhT35+MdKrnsWC0P9z21pdcIFwJCfNaBQRGNof5GfATcvhPEuUoPD4s9tB1UrtLejfY65/rPCQ2dWRvgmL6DA6bvTMMSfV3NzDfMfbWarvD8N9PWv8vPZ7Oa6hALtVSBc+zCbP0MYbrbe9A35+YHpuZjzXb+H4Ucrwu8X/057u/5S7V4iduAO8edNxCmZh/gzL9QM3DHwCfsw3kADMlKMmfN7YvYd6EJ5HpU/L5ZX9jfdVIYK6/O42bnVNxUriBhA0Gj1q6NwZuu3D+tj9NG+ED+dw+D8207so2ROeqtanYKiOtQX6BuUZ5lvtudm8UWt1sqcDLu9jdqsfPO0JRjEAH/+WUWb7hnqhnenPNd3QVPamrDk56HxVTWLszaGgDG/J9rIZeJj8mfbPgyw6gZaS4vGT8WxcPlzcpe7rIynfqtYuPbbh/UxDLR6pZdT4c7v2V//+YikJH48oz++anbgz856q4vGTyft6sPQ7PqWfI2WgVz+jNcB66k56C7tfIHyiOsZD8wf5mbOLJ4BlyE/t8tu0S7aDKQpf42tCWf9Z2FzMMDJcPb+pJ0nwbXfrjdD/UeYPfpIiIPy89D6WOj51osnufLzEH8WmWzn1yTrz+95kJ+vSWnlYng1UAv+Hagx598WQH11Wyhc/syFTBgNzvoYZ7SL17oQsoFR+w+Nr8JZvP50Q37OHxsBMWVDrBY3tRdIRHw9SHqbeuUn2377bJYPdgejrPHjP+76VengCAd+4Uf/j+tX9UCbjZfVcn/9Z5QT1fqGcm3XqdepVxfN4Pye0MSIVp4d/tyuYxZnV35uY6mmFD7Bn6H+ixZf/fUlUfI9yzbZhpGmi2Uh68jPdD3cmPniV97ac7P6Rq+/sVvc+pkguOtX3b8Nrl/Zll7hH1r/2X4i0RauouAzMOy3te0ZZmRtM/tt6cWVBsdXPUir0jiw+XPSRbBy7sFeX7K+9uxp+Q3+NL5KA61H4s8II4yRqMWD103Yw+p/xjST0YdvLB6HM78uCEu/jXkmjc2wbUFSDwntn8BA/7Oj3xZA135WYrj8W/+zAtreedE37bfrSi2JWTnCMur8rOgGy/Nq6RfZQ7D4c7uWIcdJrAJgzb+dZKq6CFw17Ah3fhInaqC9PY78rPF6Hm8FZYs/06qjC0p5t95zPUxl54ZvK8eUnzGvMqB/z9mhu7bmsqOHzv2WmXLlZ0u/PVPLu7TMjz3/1T5Mx0/SefZXmV2pXC6GuWiOhXem4//Ojz4amp8k92urDMLkzwnrePtK6VoY6keBKT8njF21tOVZ13pJNy4+od9eVaSJ1eiw7cMErkjPXDIQ5vrPoqetSS+bVAhrOK+gaXNTNuxmxX11EIZ+G2/fyW2TAnnRW1tnFZzvi6ZLktxXh+LYb+McKlOZ1hD+LwiasGnXrjh7NX/2WSC7/xnT9FG1jmbXot9ttUzZYkXvfO1JDPvts1lXLFCtlkxcdaIO+lnK8PsKjUZ/Faa/GK3rfBmxSWzw5/uSpJdidk9bK+MPQ35OKvIGGQjoM6sW98bf5eeE31XVdaRl11YD45/bKhvS+HjB0G/X2bJallVZvYgeB9SlwCxm66ap1rkn9zHtw+puWQnqy0rwn3qeZggY26OgbwpJn4fNn/Eiq+ZXsjWPO6FyrkX4n3PPhT4d/vzQ5vNK2lOjb0zWepsmrbrc20LDsN+GSNl0OTAf4UyqS4i7BxX/vguV4qZvH0bL2+quy3wTcwh9/gw1KGQgSOGyiqYTM8ozl9Qh/1Se7MWFq9/Wrj3QQ72qk5VeIjgUQ/wZr2aRGhuQwv3xVXgF4RabLA8ooSIrP7Sc+ldOhvw8qxPYgHwt4hC194Js3VLqv062u/4zfEIiIx/dLESjrwb5QXrkBXv9KlSvDvRV+DGE35++Mz8JkNOxjVu5jrUIv3/8m+MlgXhIYg6hz5/RCiBTWA3kioF+ef4Qibuq60QlQBTY/HlAfo6J0ecPs9Z/jo0h+7CYcOXnuHD7nyNj7PWrHPvtyBiwD4sKW7+tEY2h/d1+OyqG+HNMjD2/54B9WFT8ef3ncLj9z5Fhys/RMTS+Kipc+7C4GHt9Sdd+O1pVMYSh+cNiYuz5PUdeX/LfX//Zkp9jY1C/HRNj82fXPiwuPqHfjonR+fPI8+n/B/NvRxOlhuCOf46MkdfHwIU5v2d0PI3Mn7P+/CQjwJ7fs0qzrOttes/WcusCzxl7e/wO1LJnua+fxTPY5cvQA6hljDSCPEBQFYc8C//FJvbjs73/nz53G0aW4u+HeOntygdwwg/FIe+fTzuy54IABf1PRXNgF5Au9YvPn7N1es70n9V36X2/iV1COdXXfP6cZW+pdCkPwQm7fGEdcv9dKH93Fn8iF9ov9UgAzsqj8D0TI2mfJFW4k+TF08P74D0vyRwIq9ykd+lUHxR2iN2yF0J5WRhbo85wutDOoHO5LAu4ArFCErwQDwV1+Yvwo3ypBD04BFVxkmfY9r+QT/a7DITcPuEuyvKiLA8RYWzyBB9zoC1/f/IBXrwIYurv6gou+UZTha0XIvHspHMFHwAuIHakIl6qH0hvROrIF+JHnz+LPxXlUocfHmjS8Fi8EPvxKi76J58+w7WA6BFu9VBQkUEV74+/CzhD/C9fdMToD4JNnn7/p1POzQsUgB5ptcmMo34VeBRlZvJnjGdy15eZ3sQuH4ZvGD9IWgB1A2d4ovw4uL3OevuB4KwfKG/UJrx6QPK3AHXev/2Me9Z7BBf9UG5y/7F/Lq/Ct9M2ETZFR1I5XOVTSV79Tr05bn2f/uTWD9RJQr2TDr3LG0VeBAf+84nzTES4ovAD/inDrf0SsSwfiQv8Wnoh36iXJ501GXCpW/lE3MfaRBKDV+AfEp5IP4RDXvYRGnIWpGE7klb70RWwK/rjilP/PxhVBJ0wYcKECRMmTJgwYcKECRMmTJjwz+Ps7P8Ax/ySWnVMf3wAAAAASUVORK5CYII=)
Maths-General
Maths-
Calculate the time required for the principal to triple itself at the rate of 15% per annum simple interest.
Calculate the time required for the principal to triple itself at the rate of 15% per annum simple interest.
Maths-General
General
Complete the sentences with the plural form of the word in brackets.
Complete the sentences with the plural form of the word in brackets.
GeneralGeneral
Maths-
By completing scatter plots for each of the following data sets, describe the correlation between x and y as positive, negative or none.
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)
We could possible draw numerous trends for a line. But the main purpose is to describe the pattern of the values from the line. So, it is necessary to choose the best fit for the set of values.
By completing scatter plots for each of the following data sets, describe the correlation between x and y as positive, negative or none.
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)
Maths-General
We could possible draw numerous trends for a line. But the main purpose is to describe the pattern of the values from the line. So, it is necessary to choose the best fit for the set of values.
Maths-
Show the conjecture is false by finding a counterexample. Two supplementary angles form a linear pair
Show the conjecture is false by finding a counterexample. Two supplementary angles form a linear pair
Maths-General
General
Identify Text Structure.
Sharks and orcas hare a lot in common. Both of these sea animals are carnivores (meat-eaters) that hurt for their food. Like sharks, orcas have smooth bodies that can speed through the water, and lots of sharp teeth. But these is are major difference sharks are wish, while orcas are mammals! Like most fish, sharks by lay eggs: But orca babies are born live and drink milk from their mothers. Another difference is that orcas have Sony skeletons, but sharks have no bones at all-only casttly.
Identify Text Structure.
Sharks and orcas hare a lot in common. Both of these sea animals are carnivores (meat-eaters) that hurt for their food. Like sharks, orcas have smooth bodies that can speed through the water, and lots of sharp teeth. But these is are major difference sharks are wish, while orcas are mammals! Like most fish, sharks by lay eggs: But orca babies are born live and drink milk from their mothers. Another difference is that orcas have Sony skeletons, but sharks have no bones at all-only casttly.
GeneralGeneral
Maths-
Consider this simple bivariate data set. Draw a scatter plot for the data.
a)Describe the correlation between x and y as positive or negative .
b)Describe the correlation between x and y as strong or weak.
c)Identify any outliers.
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)
Consider this simple bivariate data set. Draw a scatter plot for the data.
a)Describe the correlation between x and y as positive or negative .
b)Describe the correlation between x and y as strong or weak.
c)Identify any outliers.
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d/7kR0lC2OA4UcnNrf8xfcDknU/fXF7gNEWX2PMZkL0LmJ5QbGn+GpQfd34c63hznwfhvobm63mp8/Hhgo30caRrm0/ty5h2sIp//kMqd5pM/PTSuZvNWX3EvwM/up3m/Vp3GnZ5e+syJv9vBp4tIhw3OIVX3s9aVcAHQAt3K/p7OGqzstkgk2utOB396+zzJ9f3wO0XfuIjgVj04uVs3XKvUg3TrO3uso3W8udiyb3zMliXLmvzhKdMy7SNftbVZ+25sb40xUSmwdITwzEQe7W9bMKEhlg9fO+kFklYqxXkw2eaE+zKVlM7GYN/CrdFWfYU6F3fb4ucmr0l5Veac+1dvdnXq3d7FcXh5j0hc80VEan3kA0V4O8w+rKz8Frm4TuDtIIfDANSudWI6XiTvYqdRRDq47iErHBHkDn+bd3vuPg96r8i2/UyErv74/Xh1vrCufWZ9x9voQO5aDK59pqeBSchif8iuh416zcq4+jtFBXDohj6mpb/VnkNPXQH97gmq4wFnPpEY/ang7PoBP9UTfKe9l75MVn9F6pW+108Ead3xp9+MyB5f6ex3+siyECc4LO5qzbzUqKq6DP36hYjQRfjwLdOZCqG25Tj4jq33X+QY/UlHaNLBvF/sRJ60OVkeiIoTAbUz6Pd6FeFVY71FW41R8+tX9MsTV4mUmPwLUHluitdTKE+GlQ6YlU8XvFAm8myiUR//udHu5g7+H9759u7ni+VXXsmVe0759xfPo/kL7triefZsN2LfTQPrrJdOB+tNVgZ+tLznq8i+0b1/1fMkrRfW3ni95rfqcvf2r9m3rSw70+/kkkeHOD0sKWcWfMPgbjOPPQbgeP/+vjD+n2HViENc8/znFKv9h9PbfToW/dPz5avX5iuPPQ/PDrC85/u35YUkiH9rfE5BiksnV+Hlw/XMaXJGfe+dLJsP5/Bw83fTP/Ixk9lP+zPR2qehnts6ymPN2vfnbmL0T2pvlX2cRtvcYOv95VRTOdnk1JTTCBgdD5z/zwjnIj6t1ExG26uvpz6vMS766IM338P5/n5/rbL38KZ1SiiQUlXVoFenNJ+GbqbfWVGQNibH81NOfhYRg/RljE7kEQUMX0/T5GWUDc9OQpEVo/frN/DALzJpyNjdrGAUjJZExJye7/PzwUuV5vnX33EaSlO9+Ff86BvgZL93NBvl0I+k0fNFQn5/ronnprF/AsgU5ZxH2CPT4Gd02zv6eNzeLjTrdMjymnv6MNvmsfHM2j8FFOSvzKrTH5a2vggJIJX3tVqgJ3Uj5SsJzyuVn9NJC+fN2LBffQahZ+RIolMfPv6BUb3srJMX+nUoeujbjE/1ZreA9LvtGzT2LusgQ9GfrU5Btk2Ukz7vLasUiK50HF2KAn0GwbuFfEHVHw09X8fVnvCd6L5lTPu1bkmVN6cR+GVz9GastN51teVDxnasEDF5c2+NnSRraNFm3j8Gn0817Q0M7A57+zAi05oJ09qJEjWqG63mEnOrys5CFXDB9slMHHCi7aULXUPr7e+pS7RMMKqIc39rTn/3zJfeVrVC4nkYe9AF+/ihpqFiubjCiednNKFHTnAY2WYCB/UkKtQm3vQHhzPGCiyp4H+S9x8987m6MiooCokAxtmPz+HmlttvqPMG17uQX4Tti+ftvI5qJnXsmonihSDyHH5Po6s+rqT6gcD//kGs51+VhWZLQrqKzvmqlT3LnlbPhlSiekHgQwdsS++dXCfHNb2RXdB5li7RP9/fEUMO03KKIvSebs76Kmw7Uosqdcg7RR9hwsb+/55rSsrMVBa/uVeUWr/aAy8uxLJ3aUx+8LQ0X5uyWugo/B7Vn3y5K2qFMvNOpyzqbEF0If//tfZ9J+GOczRVd/VmWui2CRDyGjhpT4+oqeDdHl591GgnihMpeQ0uDQU9/VudLWq+ByCKdpQb87FZoR39WqC2Rsdc4OfYBZM5tNRCmiDw0bsu1K+yWrUHo8TNv/oGy8lGfs61p7sN7A3tnP4NVrzmyFCY24XpEz76dlc2z9X6AtcGjgp7+LGjb22Mga4som0Q49VkUM12fxTQ/9sKROSIcYxJ8GMKAfRttun0ZQe/jCNWzb6tjaZ0EXNwHE4nB5/tv43X5BqlZv0YfXBranwRNnf42pGkMfvb390SNFLKz+fyqyb/p9A0/cnLt6M+3ZbW/qSf9MFffIvS3e+djzLr8bFGEq2XoQKxPQ5Z53rpjAYs2z6tmH5h2AGd/En28pPI0ObWWm/rwprtauAluDwfmh/GmKxWfg1Cb8J2i+vuTYPrmNLLiMX/EuzqCsfmM/bfVge4cF+H2Gx9D48+IuBVqF+WQZo+ff0ko4/vyY//hZ5KbneizXG/DHIB92dGfIelaqYZXFr6gz+Tea7O/jiF+9nU9wcjAyMgX4Y0/83dyr46v6uRLTV/bbV66I4CXwNGfEWSLTjh6MgzAo42KF+pzaLnwz69SUjhhLui0nb15THoJBvm5UyJwXW3p/omQ8FHMc+aH8TbfLDdx+gNdDM0PY/6IAY1xioVn3+aKXJYdfgadwrT2WV4FVjNn/Lk+AJEVFN59XYVF2C7Y52dQwrwyjut1WdLgOQP98edF0UIjb28AEEO9JnkZvA+nY9+u56XpwX3U5x3NtaUDQY4FxrX0+BkxkrfezoNKqODDQXE26/GzU/TxOi8pLd6r2VNooThr/7B9md+Ht1I9DPAzevf2+I7Ez+7+nnofYNCfT6n843jUJPCzX/O+CGf+Nr/PVQ8YqrXXu1kVwUZnVZ+9YuKeXwXADOpd+DkIQ/PD1Jazngg11LXQToejP5+aWZqfrNm8yl/5hKsRn8D67M/frtUo4tyvvPV7vzH+KrJP9GdRQBxqb5syDx1WP2v+NprGOujRQV9/xtIve0p/Dsw3wL6rrIpMF8Su/ly3W1NwIGEDS6Qz/rwvjTVWuqNwappEaBkBEOK2fVBK+nNvJt/KPPScycH524v53JcBtcHz5N36fLBGr03+YeiTVdk2zcGdzXIJZO72t/GNkFXffgJlo0sGF2BIf3aOhtk15nwuMe21kV/FWfwM7UfHFhwL2Bl/1vinufUiXz1tY4w/d/mZtwXjnNPZvTxaL1cd/TkwQWX3mBlWGbPlpHJ7ojUNNoYBhvTn/o7lAgpL4Agq8s9/VoDWoycEK/tk8DX09GddQr7pbo7FJCvW0BUIHn/eD5iU5VtvGAxnb6E9xJ7+jJ+c858hi4xRINuG7vZ8Fj9Ddz9C97CH2m/4Fvawpw7i8HN3/22xrqrD/byt8rI6zigRj7aBhNhC867Lz7y60xzgHcxVxzn4w5+/DTxAB2qunIUOQPfOf9aQ9749STWV1ncpHP15Z8+XFCT3wlXMHVoslh4/K9Rtr9MBHYLAEZY+PwvgZ+tVgGJnsigrQ7uHZ/Gzqs+B7cYQfH5GA2cuQNcgtIYBHPt2/TMrMjXlsqLy2OtgZjAaN/mLeXAxnPlhtR0lFdOyM+yBijhDf6T1Ok3ZbGD8GRqV0BQcXv/8T1+vrIMPHHP62zgrtUqxI7Nb8+SIYhteIOUAP6PX/vgGO4QKNXC+pMPP0PU3qtP+rg3sdZxx/rNpP+L3t73520fWEvWPzr+0i2LfZs78MAgP3+Cf5fQj7Sat7uhM2uBT67Pu/iRqOomSCbUdfQW9aInEJDRJe+c/Z91u6QnSGzL4Ogb1Z7HuNBP2/5Cb0Frmzg/jlbbb8MqTlLfh07eh3zLAz102Mz78Eto3HebnrkBWTuDn0FGPz+eHAYCfQ4dlh+DMD8M1JWvGmMyUqcgeMAmSpxh/1jiOP5uYZKWMRjJ4fZCrP6uMUgWPm9EpodJ68kQyJvesCF6d+fvxZ5N8WJ+Bh2jwhJKdw89IShUg/6bpTUclmOrpQBYGmwXc9VWgo6tsKXTyncrETd20/ar4Zbj6szkvUBo7JaQctPjMyEn7NP5F9M+XpMehHStUoWelC0pCy8Q5/IwnJO+rFeFw7Nt1A/psWd69abMzI6alx8ia/sIwtP45K6cqu+pGK9Fo09a4PoR3Q7z1z2s1wwniUA/lAWrWpMn1NsWlmZocgh4/n8wcHITCojgUXCyMfEFw+Zndlw1HmTrlHyBJsVP2AcLQ/jXcLOCdX/VjOp1gPtfGymOZEJzEGB1wx5+ZOsoPZeZQTrFui9XNDy2UpMFj6j1+xuI0u40dtGw/SMUwlsGnuuNz5ofxp4YcKIve4+7qz3h9aAEEXipJ94bXcF28ts26N7vqqxjiZz7VvLWoDjoREd3AFVz0/fXP+JZsio1eRvhrXUGPgBMQUl1t8Apel59xLRtCCr1WnR/mDLTPqjqQJgufRejat1fqTESS2TMRQSj4L2Q1bwmNcHCcvz9JTWmx+a7llJVir2e+phFEAjjjz5yAAMRSsXipoK3HSyUUNIn6WQj88yUXa9I2ZmuQfWmaW7WbRtHfzePLOMe+LRBCzyjmKXgGjn37Aa12KwTXs4oHcV2HsdhB3OExD/Gz2GnJxWJhUkBwXgdPt1OlrlvJVDX7h+s6poSCLBS7hx1ItVs9BOcdcc6XVGn1AGErv+BKqN2CsWPcQfDs25BS7JRUiKtzT0XN2KI/VvZ19PYP2/F/aqNECCULRotaRRgBzv5huGZ7VlsJMGJWqP3xUQh8fsbiAUG51oquKhNanBXnMdZ+nGXfTgOMZqHa/7kY4udE2N+Fc/yZ6OnPqXDF/T1de5gHXTA9e8/l+Hz/sFgx/XH/sLi17azx50T4w/nPkdFf/5wMjn07LXz9ORkG7dtp8Mf6HBcD66vSoG/fTod/kZ8H11elwRX52bVvJ8XV+Nm1byeFt39YSlxvv97e+HM6/Jv8jEInxp4NHr7Y+Fzs766kQyh+DpzHeS5EczV+FsGrmc4GmyUYsxnEenaljtTQeNV03lwHG5JX1nvEN/sZFe/NN5LfvytPHxv7ioY2J9Y3hI/owkV9LEvyaP0DOEYVQ7py9g7BOOkXI9gBvG6roWxSiCXMEW3edgpF7NC7uIcy8buwI8c779XnjLQHuE7u0BLl7NNoLwBpCAQNIeuXHsOxgzkqNnDqS/0D8wdqMOuLL/Wh3qdHjw7YhG6D1f+Qvf14nUL4yks50kBIxxsdKkigkk5dAHgzQqpvg16tST7lMx8m2dSn+hfgN9Zp0YIu0ujwICD9poPX7+Z78436Ql9ayIteAEJM+MeIlCz6ZX/ykZb2Dy6CigCKnw7GCmUe6tTT8R7j11KGvCBIiMn47AXB2hdAf6cj7P7VZa+W9Ka1PyD0vEKo647wHge6T6H+DYvV7qy/GIL5u50Ow8elYX4CJ6KVk5pRo9w9O8H/EadfXeB+C/V1J5d6BedrTufxA5S/3wG+tvIeI4I/ucidoIL8FCayi18nwI1xQwhLu6NDMQYNR4wYMWLEiBEjRowYMWLEiBEjRowYMWLEiBEjRowYMWLEiBEjRowYMWLEfypubv4ffirrZ2V4FxoAAAAASUVORK5CYII=)
Maths-General