5. Indicate if each of the following statements about the simple linear regression model is true or false and explain why. (a). The simple regression line estimated by the method of Least Squared passes through the sample mean of dependent and independent variable. (b). The slope estimator of the Least Squares regression line is free of measure. (c). The sample covariance between the residuals from the least square regression and the explanatory variable is zero. (d). If the values of x more widely spread out, the estimate of the slope coefficient is less concise. (e). Adjusted R-squared will always increase when an additional independent variable is added into the regression.

If a regression line for two variables has a small positive slope, then the:  variables are positively associated? variables are negatively associated? association of the variables cannot be determined. variables have no association with each other.

4. We would like to determine how a man's weight can be used to predict his systolic blood pressure. We measure the weights (in pounds) and the systolic blood pressures of a sample of 17 men. The data are plotted on a scatterplot and it can be seen that a linear relationship is a reasonable assumption. The least squares regression line is calculated to be ŷ = 125.561 + 0.145x. The ANOVA table (with some values missing) is shown below: |Source of Variation | df | Sum of Squares Mean Square F Regression Error Total 26.46 617.82 (a) We would like to test whether a linear relationship exists between a man's weight and his systolic blood pressure. We will conduct an analysis of variance to test Ho: B1 = 0 vs. Ha: B1 + 0 at the 5% level of significance. Enter all missing values in the ANOVA table. Calculate the value of the test statistic, find the P-value of the test and provide a properly worded conclusion. (b) What is the value of the correlation between weight and systolic blood pressure…

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You forgot to JUSTIFY THE ANSWRRS LAST TIME

The accompanying scatterplot shows the relationship between the age of an internet user and the amount of time spent browsing the internet per week​ (in minutes). The accompanying residual plot is also shown along with the QQ plot of the residuals. Choose the statement that best describes whether the condition for Normality of errors does or does not hold for the linear regression model. Choose the statement that best describes whether the condition for Normality of errors does or does not hold for the linear regression model. A.The residual plot displays a fan​ shape; therefore the Normality condition is not satisfied.B.The QQ plot mostly follows a straight​ line; therefore the Normality condition is satisfied.C.The scatterplot shows a negative​ trend; therefore the Normality condition is satisfied.D.The residual plot shows no​ trend; therefore the Normality condition is not satisfied.