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Statistical Analysis and Interpretation of Linear Regression Models Using R - Document preview page 1

Statistical Analysis and Interpretation of Linear Regression Models Using R - Page 1

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Statistical Analysis and Interpretation of Linear Regression Models Using R

Application of linear regression analysis and statistical modeling using R.

Christopher Lee
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12 months ago
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Statistical Analysis and Interpretation of Linear Regression Models Using R - Page 1 preview imageStatistical Analysis and Interpretation of Linear Regression Models Using R(a)The plotof the regression lineobtained using R is shown below.It is also showing thedistribution ofYforX= 10, 20, 40.(b)The parameter β0 = 200 indicated that the average value of the dependent variableYis200 when the independent variableX= 0.
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Statistical Analysis and Interpretation of Linear Regression Models Using R - Page 2 preview image
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Statistical Analysis and Interpretation of Linear Regression Models Using R - Page 3 preview imageThe parameter β1 = 5 indicated that the increase in the dependent variableYis 5 units forevery one unit increase in the independent variableX.(c)If the slope of the regression model β1 = 0, then it can be concluded that there is aconstant growth β0 in the response variable for whatever variation in independentvariable exists. It indicated that there is no significant linear relationship betweenXandY.(a)Using the following R-code we can generate 100 random observations on predictorX.(b)Using the following R-code we can generate 100 random observations onresponsevariableY.(c)Using the following R-code we can construct the scatter plot forXandYgenerated aboveand calculate the correlation coefficient.
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Statistical Analysis and Interpretation of Linear Regression Models Using R - Page 4 preview imageThe correlation coefficient betweenXandYis,r=–1.
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Subject
Statistics

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