I need help on #9, 10, and 11 please all the papers go hand in hand you need them, are as follows:Y= 126.22-1.20 (35)= 84.2a1. Draw the scatterplot for the relationship between weeks on the market and selling price. Don't forget toin describing the relationship between condondo is on the market before it sells. He has collected a random• Input "Weeks on Market" into LI and "Selling Price" into L2.Press (STAT]Use arrows to go over to CALC menu• Then use arrows to go down to 4: LinReg (ax+b)• Depending on which version you have you need to tell the calculator which lists your data is in.•Then press the [ENTER] keyWeeks on Market Selling Price (in thousands of $)old within the past three months in their area. The data is shown below:184812551732. What is the value of the correlation coefficient?-O,701433615676.53. Correlation is an indicator of strength and direction. Does your interpretation of this correlation agreethe answer for strength and direction that you found by reading the graph on page 1? Explaip6.53Selling price and weekS on marketCOa o 9o hand in hand negativelyand has dmodente Col relation andTheie is no Outlien,133254794903227107842354161024. Using your calculator output from LinReg (ax+b), to get the coefficients for the regression line.- Draw the scatterplot for the relationshin between weeks on the market and selling price. Don't forget tolabel the axis.a slope -.20Scatter plot140b-y-intercept-26.22Y: 126.22-1.20x5. Write out the regression equation:6. Using the regression equation, predict the selling price (in thousands of $) for a condo that has been onthe market for 35 weeks.130Based on the graph, what is the....120Form: Linear ASociation110Direction:7. Using the regression equation, predict the selling price (in thousands of $) for a condo that has been onthe market for 10 weeks.100Strength: ModenateY=124.22-1.20610)= 114.2290Outlier(s): one.List (if any) or say "none"8. Using the regression equation, predict the selling price (in thousands of $) for a condo that has been onthe market for 72 weeks.39,8280%3DY-124.22-1,2067a)=7040Im102o 30weeks on masketSelling 9. Find the proportion of variation in selling price that can be explained by its linear relationship withnumber of weeks on the market.10. It turns out that the condo that has been on the market for 23 weeks and is selling for $54 thousand is anoutlier. To remove this outlier, go back into your lists. Find 23 in L1 and delete it (using the [DEL]button). Then find 54 in L2 and delete it.r=Now, run LinReg(a+bx) again to recalculate the correlation coefficient.11. Did the correlation (r) get stronger or weaker when you removed the outlier? Explain.

Question

I need help on #9, 10, and 11 please all the papers go hand in hand you need them, are as follows:Y= 126.22-1.20 (35)= 84.2a1. Draw the scatterplot for the relationship between weeks on the market and selling price. Don't forget toin describing the relationship between condondo is on the market before it sells. He has collected a random• Input &#34;Weeks on Market&#34; into LI and &#34;Selling Price&#34; into L2.Press (STAT]Use arrows to go over to CALC menu• Then use arrows to go down to 4: LinReg (ax+b)• Depending on which version you have you need to tell the calculator which lists your data is in.•Then press the [ENTER] keyWeeks on Market Selling Price (in thousands of $)old within the past three months in their area. The data is shown below:184812551732. What is the value of the correlation coefficient?-O,701433615676.53. Correlation is an indicator of strength and direction. Does your interpretation of this correlation agreethe answer for strength and direction that you found by reading the graph on page 1? Explaip6.53Selling price and weekS on marketCOa o 9o hand in hand negativelyand has dmodente Col relation andTheie is no Outlien,133254794903227107842354161024. Using your calculator output from LinReg (ax+b), to get the coefficients for the regression line.- Draw the scatterplot for the relationshin between weeks on the market and selling price. Don't forget tolabel the axis.a slope -.20Scatter plot140b-y-intercept-26.22Y: 126.22-1.20x5. Write out the regression equation:6. Using the regression equation, predict the selling price (in thousands of $) for a condo that has been onthe market for 35 weeks.130Based on the graph, what is the....120Form: Linear ASociation110Direction:7. Using the regression equation, predict the selling price (in thousands of $) for a condo that has been onthe market for 10 weeks.100Strength: ModenateY=124.22-1.20610)= 114.2290Outlier(s): one.List (if any) or say &#34;none&#34;8. Using the regression equation, predict the selling price (in thousands of $) for a condo that has been onthe market for 72 weeks.39,8280%3DY-124.22-1,2067a)=7040Im102o 30weeks on masketSelling 9. Find the proportion of variation in selling price that can be explained by its linear relationship withnumber of weeks on the market.10. It turns out that the condo that has been on the market for 23 weeks and is selling for $54 thousand is anoutlier. To remove this outlier, go back into your lists. Find 23 in L1 and delete it (using the [DEL]button). Then find 54 in L2 and delete it.r=Now, run LinReg(a+bx) again to recalculate the correlation coefficient.11. Did the correlation (r) get stronger or weaker when you removed the outlier? Explain.

Accepted Answer

Since you have asked multiple questions, we will solve the first question (Q9) for you. If you want…

9. Find the proportion of variation in selling price that can be explained by its linear relationship with number of weeks on the market.