problem 1, two parts Data 2 3 ANOVA Regression Residual Total 4 5 Intercept 6 7 X 8 9 10 161 203 235 176 201 188 228 211 191 178 To the right, in the gray colls, is the regression output for the data above (y is the dependent variable) This output is generated by the regression routine in Excel, and is similar to what would be produced by Mintab For HW7, you will learn the math behind how these numbers are generated and recreate this output The Week 8 notes, especialy 8-3, will give you the equations for each coll Your answers will go below, though you will need additional calls for calculations Each call should have a function or equation not just a number The result should match the output to the right, so you can easily check your work Y 100 200 241 163 197 Part a. Fill in the yellow cells below. These must all be functions or equations. Do not just type in numbers. Regression Statistics Muple R R Square Standard Error Observations 193 200 $$ 189 160 201 Coefficients Standard Error MS 1 Stat F P-value Significance F Lower 95% Part b. What can you conclude from this regression output? Is it a good fit? How do you know? Upper 95% SUMMARY OUTPUT(From Excel) Regression Statis Mutiple R RSquar Standard Error Observations ANOVA Regression Residual Total Intercept 08100 0.0657 15.1005 10 1 8 9 Coeficients 22.36 0.86 SS 3631.9 18242 6456.1 Standard Error 42.05 0.22 MS 3631.0 2211 1 Stat 0.02 3.90 F 15.9 Pvale 0.62 0 Significance F 0 Lower 95% -76.68 0.36 Upper 95% 121.4 1.36

In R there is a dataset called diamonds that contains measurements of about 500 diamonds sold in the US. There are three variables present: price (price in US dollar), carat (weight of the diamond), and table (width of top of diamond relative to the widest point). The attached image is a screenshot of the R dataset with the regression table and all that.   ANSWER THIS QUESTION IN WORDS: Discuss the regression between the variables table and price. You should address the explanatory variable, response variable, correlation, and sign. You should interpret the slope, the t and p-value, and how much is explained by the response.

PLEASE ANSWER 4,5 AND 6

What is the explanatory variable,   what is the response variable. What is the regression equation for this data.   Plot the regression line in your calculator and draw it on your graph.   Interpret in context the slope.   Interpret in context the y-intercept.   What is the residual at 45 years of age.   Plot the residual plot on the axis to the right