Find the linear regression equation for the transformed data. x=1,2,3,4,5 y=13,19,37,91,253 log y=1.114,1.279,1.568,1.959,2,403
Talja [164]
Answer:
The answer is OPTION (D)log(y)=0.326x+0.687
<h2>
Linear regression:</h2>
It is a linear model, e.g. a model that assumes a linear relationship between the input variables (x) and the single output variable (y)
The Linear regression equation for the transformed data:
We transform the predictor (x) values only. We transform the response (y) values only. We transform both the predictor (x) values and response (y) values.
(1, 13) 1.114
(2, 19) 1.279
(3, 37) 1.568
(4, 91) 1.959
(5, 253) 2.403
X Y Log(y)
1 13 1.114
2 19 1.740
3 37 2.543
4 91 3.381
5 253 4.226
Sum of X = 15
Sum of Y = 8.323
Mean X = 3
Mean Y = 1.6646
Sum of squares (SSX) = 10
Sum of products (SP) = 3.258
Regression Equation = ŷ = bX + a
b = SP/SSX = 3.26/10 = 0.3258
a = MY - bMX = 1.66 - (0.33*3) = 0.6872
ŷ = 0.3258X + 0.6872
The graph is plotted below:
The linear regression equation is log(y)=0.326x+0.687
Learn more about Linear regression equation here:
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It’s Negative below the lines the touching points do not meet
Answer:
Explained below.
Step-by-step explanation:
Consider the variables height and weight.
It is usually seen that taller people are heavier than shorter people.
So a regression analysis can be used to specify this belief.
The statistical questions that are being asked here are:
- What the independent and dependent variables?
- Are there any other factor influencing the dependent variable other than the independent variable?
The variable <em>Y</em> is considered as the dependent variable and the variable <em>Y</em> is considered as the independent variable. And the main purpose of the regression analysis is to predict the value of <em>Y</em> when the value of <em>X</em> is given.
The linear regression model can be used to predict the past and future value of the dependent variables provided that the independent variables for those times are provided.
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