Maths-
General
Easy
Question
Find the solution of the system of equations.
![y equals 0.75 x squared plus 4 x minus 5](data:image/png;base64,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)
y = 4x - 5
Hint:
Hint:
A polynomial of degree two have at most two real solutions. Factorization is the process of factorizing an equation or an expression.
We are asked to solve the set of equation.
The correct answer is: We are asked to solve the set of equation.
Step 1 of 3 :
The LHS of the given set has the same value. So, we could equate the RHS of the set. Thus, we have:
![table attributes columnalign right left right left right left right left right left right left columnspacing 0em 2em 0em 2em 0em 2em 0em 2em 0em 2em 0em end attributes row cell 0.75 x squared plus 4 x minus 5 equals 4 x minus 5 space space space space space space end cell row cell 0.75 x squared plus 4 x minus 4 x minus 5 plus 5 equals 0 end cell row cell 0.75 x squared equals 0 space space space space space space space space space space space space space space space space space space space space space space space space space space space space space space end cell row cell 75 x squared equals 0 space space space space space space space space space space space space space space space space space space space space space space space space space space space space space space space space space end cell end table](data:image/png;base64,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)
Here, we multiply the equation by ten to remove the decimal.
Step 2 of 3:
Solve the equation to find the variable of x.
![table attributes columnalign right left right left right left right left right left right left columnspacing 0em 2em 0em 2em 0em 2em 0em 2em 0em 2em 0em end attributes row cell 75 x squared equals 0 space space space space space space end cell row cell x squared equals 0 space space space space space space space space space space end cell row cell x equals 0 straight & x equals 0 end cell end table](data:image/png;base64,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)
Here, the solution is x = 0 with a multiplicity of two.
Step 3 of 3:
Substitute the value of x in any of the equation to get the value of y :
When x = 0,
![table attributes columnalign right left right left right left right left right left right left columnspacing 0em 2em 0em 2em 0em 2em 0em 2em 0em 2em 0em end attributes row cell y equals 4 x minus 5 end cell row cell y equals 0 minus 5 space space space end cell row cell y equals negative 5 space space space space space space end cell end table](data:image/png;base64,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)
Thus, the required solution is (0, -5).
The multiplicity of a value is the number of times it repeats itself.
Related Questions to study
Maths-
Consider this simple bivariate data set.
![](data:image/png;base64,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)
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.
Consider this simple bivariate data set.
![](data:image/png;base64,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)
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.
Maths-General
General
Choose the correct definition of "Mech anise"
Choose the correct definition of "Mech anise"
GeneralGeneral
Maths-
A tomato grower experiments with a new organic fertilizer and sets up five separate garden beds: A, B, C, D and E. The grower applies different amounts of fertilizer to each bed and records the diameter of each tomato picked. The average diameter of a tomato from each garden bed and the corresponding amount of fertilizer are recorded below.
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)
A )Draw a scatter plot for the data with ‘Diameter’ on the vertical axis and ‘Fertilizer’ on the horizontal axis. Label the points A, B, C, D and E.
b)Which garden bed appears to go against the trend?
c)According to the given results, would you be confident saying that the amount of fertilizer fed to tomato plants does affect the size of the tomato produced?
A tomato grower experiments with a new organic fertilizer and sets up five separate garden beds: A, B, C, D and E. The grower applies different amounts of fertilizer to each bed and records the diameter of each tomato picked. The average diameter of a tomato from each garden bed and the corresponding amount of fertilizer are recorded below.
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)
A )Draw a scatter plot for the data with ‘Diameter’ on the vertical axis and ‘Fertilizer’ on the horizontal axis. Label the points A, B, C, D and E.
b)Which garden bed appears to go against the trend?
c)According to the given results, would you be confident saying that the amount of fertilizer fed to tomato plants does affect the size of the tomato produced?
Maths-General
Maths-
Find the solution of the system of equations.
![y equals negative 5 x squared plus 6 x minus 3](data:image/png;base64,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)
Y = - 4x - 3
Find the solution of the system of equations.
![y equals negative 5 x squared plus 6 x minus 3](data:image/png;base64,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)
Y = - 4x - 3
Maths-General
Maths-
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?
![](data:image/png;base64,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)
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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)
Maths-General
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,iVBORw0KGgoAAAANSUhEUgAAAH8AAAATCAYAAAC5i9IyAAAACXBIWXMAAA7EAAAOxAGVKw4bAAAABGJhU0UAAAAQ3ZOC+gAAAilJREFUeNrtmNFHQ2EYxo+ZTCYyM9lFzCRJN+kiXSQmSSaRTJJ019X+gXSRSBe76i67SBJJktlNkkwSmUkXifQ/7GJmYj0fz9iOszna+c4+fefhx3Z2nPOe732/9zzvDON/q05q4AnEDU/ayQd2QNFbCn1V9ZZAT82CR28Z9NMQ3/kxCdeeAVegTG9RAusKFXyBHa/COGM6JX4E5FgAMiS6SQoE+X2MhZbq8XPPg3cwRc/jB9vgA0R02fF5MODyfYfBm8QJxo5e2+zyVXDUfCAL1ixOTIMDBZMqKvjQ4vg+f2tIJH5UIXNpN24nkv/TYfJpmXo2zdUA9YNPEOowP3dCtoqm9rUFjm3E6Yam2fr/GrcTyf8Ck226UqX5wCK4NJ20B3YVbukLIMPPCXCvSFwB8EIjKCNuu8kX7f2bps9Hllh8teYTQzQHDYVZOQGJi+RE97jjw5XadCi34xkENzRbTsXdTVziHg98BVW5wePmnW+YDmT4vpe5UE4ozSqeUGDHx5j4uOS46w51phYV+AAx7nqf4k6+MV9nFBirhKk8oU+SHXe3yZ+jwWzRKUiCc7CheOJFgd5ysYMca0I9iiXCdup3Ke5uky+mt6hVK8pKnE+dUpgjXPOiCSNz1qN4cjbHSafitpt8YSRXQB+/R2ngk1YnL/PCSYUTLx7k2qpyoQs6abdlx/f0Iu4EzZ6Y98u8T1uPIZL+bHjSUnn+OeFJM43z3eVJI/0C696qmGimGPUAAAC+dEVYdE1hdGhNTAA8bWF0aCB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMTk5OC9NYXRoL01hdGhNTCI+PG1pPnk8L21pPjxtbz49PC9tbz48bW8+JiN4MjIxMjs8L21vPjxtc3VwPjxtaT54PC9taT48bW4+MjwvbW4+PC9tc3VwPjxtbz4mI3gyMjEyOzwvbW8+PG1uPjI8L21uPjxtaT54PC9taT48bW8+KzwvbW8+PG1uPjk8L21uPjwvbWF0aD7neoVfAAAAAElFTkSuQmCC)
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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)
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,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)
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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)
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