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Round all values to the nearest single decimal point. Note that due to rounding you will not get SST-SSR+SSE exactly, but it should be fairly close. A researcher collected data on the hours of TV watched per day from a sample of five people of different ages. Here are the results: Age TV Hrs 43 2 30 3 4 4 20 3 What is the independent variable in this study? C TV Hours C Age Round all values to the nearest single decimal point. Note that due to rounding you will not get SST SSR+SSE exactly, but it should be fairly close. A researcher collected data on the hours of TV watched per day from a sample of five people of different ages. Here are the results: TV Hrs Age 43 2 30 20 Draw a scatter diagram for these data. You do not need to draw a trend line, but be sure to label the axes Upload Choose a File Round all values to the nearest single decimal point. Note that due to rounding you will not get SST SSR+SSE exactly, but it should be fairly close. A researcher collected data on the hours of TV watched per day from a sample of five people of different ages. Here are the results TV Hrs Age 43 30 2 4 3 20 Calculate the least squares estimated regression equation using simple linear regression. Give your answer in the form of an equation in the correct format showing how you would use the value of the independent variable to predict the value of the dependent variable. Upload Choose a File Round all values to the nearest single decimal point. Note that due to rounding you will not get SST SSR +SSE exactly, but it should be fairly close A researcher collected data on the hours of TV watched per day from a sample of five people of different ages. Here are the results: TV Hrs Age 43 30 2 4 3 20 Create an ANOVA table. Using a .05 use the results in the table to give a conclusion about whether or not there is a statistically significant linear relationship between the two variables. Your score will be based on the format and content of the table as well as your conclusion. Upload Choose a File

Answer 1

ndependent variable- Age

4 10 20 30 40 Age

Let TV Hrs-Y = Dependent variable Age = X = Independent Variable Xi= 24, Y = XiYǐ 401. x4-3658 Least square estimates Slope = bı =-=-79/778-0.1 Intercept-bo-Y-biX 40.1015 24 6.4 egresson equation: Y = 6.4-0.1X Suppose Age30 then predicted TV Hrs 6.4 0.1 303.4 hrs

SST Syy 18 r correlation coef ficient betweev X and Y-V SArS 0.6676 SSR Sy-0.6676)2 18 8.0 with df1 MSR- SSR/1 8.0 SSE-SST - SSR 18 - 8.0 10.0 with df-5-23 MSE SSE/3- 3.3 F - ratio MSR/MSE 8/3.3- 2.4 FcritFo.05,1,3 10.1 AVOV A table SOURCE SS df MS F - ratio Fcrit Regression 8.01 8.0 10.1 2.4 Error 10.0 3 3.3 Total18 .0 4 Since F-ratio < Fcrit so linear relationship between twovariables are not significant