Thank you for any help. Step 2: It was determined in step 1 that one of the predictor variables should not be included in a linear multiple regression model. The model that willbe created now has k = 3 predictor variables.y=x+B₁x₁ +B₂x₂ + B 3x3 + eThe hypothesis statement isHo: B₁=B₂=B3 = 0H: At least one of three B's is notCreate a linear multiple regression model to predict the 2016 6-year graduation rate using the three remaining predictor values. Let the significancelevel be x = 0.05. Save the standardized residuals from the analysis so that the assumption on the random deviation, e, can be verified with a normalprobability plot.zero.To create a normal probability plot, also known as a Q-Q plot, of the standardized residuals, you will need the normal_scores.csv file that containsnormal scores for a n = 30 sample.Save the Standardized Residuals from the analysis, sort them in ascending order from the output and pair them with the normal scores from thedocument to form (normal percentile, Standardized Residuals) ordered pairs for the normal probability plot.Create and upload a normal probability plot. (Submit a file with a maximum size of 1 MB.)Choose File No file chosenThis answer has not been graded yet.Does the plot indicate that the assumption of normality and constant variance, o2, of the random deviation, e, distribution is reasonable?Yes, the plot is quite straight.No, the plot is not straight.Step 3: Find the indicated values and report them below.Regression sum of squares (SSRegr), rounded to four decimal places is 0.8147.degrees of freedom for SSRegr: k =k=Residual sum of squares (SSResid), rounded to four decimal places is 0.1686.degrees of freedom for SSResid: n - (k+ 1) =Compute the F test statistic using the rounded values reported above. (Round the statistic to two decimal places. Note that your number below willnot match the F statistic from the output exactly due to using the rounded values above.)SSRegrF=numeratorSSResiddf denominatorThis test statistic has an F probability distribution with df numeratorand df denominator✓x.(Pick the smallest number the P-value is less than).Step 4: The test statistic found in step 3 has a P-value

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Thank you for any help. Step 2: It was determined in step 1 that one of the predictor variables should not be included in a linear multiple regression model. The model that willbe created now has k = 3 predictor variables.y=x+B₁x₁ +B₂x₂ + B 3x3 + eThe hypothesis statement isHo: B₁=B₂=B3 = 0H: At least one of three B's is notCreate a linear multiple regression model to predict the 2016 6-year graduation rate using the three remaining predictor values. Let the significancelevel be x = 0.05. Save the standardized residuals from the analysis so that the assumption on the random deviation, e, can be verified with a normalprobability plot.zero.To create a normal probability plot, also known as a Q-Q plot, of the standardized residuals, you will need the normal_scores.csv file that containsnormal scores for a n = 30 sample.Save the Standardized Residuals from the analysis, sort them in ascending order from the output and pair them with the normal scores from thedocument to form (normal percentile, Standardized Residuals) ordered pairs for the normal probability plot.Create and upload a normal probability plot. (Submit a file with a maximum size of 1 MB.)Choose File No file chosenThis answer has not been graded yet.Does the plot indicate that the assumption of normality and constant variance, o2, of the random deviation, e, distribution is reasonable?Yes, the plot is quite straight.No, the plot is not straight.Step 3: Find the indicated values and report them below.Regression sum of squares (SSRegr), rounded to four decimal places is 0.8147.degrees of freedom for SSRegr: k =k=Residual sum of squares (SSResid), rounded to four decimal places is 0.1686.degrees of freedom for SSResid: n - (k+ 1) =Compute the F test statistic using the rounded values reported above. (Round the statistic to two decimal places. Note that your number below willnot match the F statistic from the output exactly due to using the rounded values above.)SSRegrF=numeratorSSResiddf denominatorThis test statistic has an F probability distribution with df numeratorand df denominator✓x.(Pick the smallest number the P-value is less than).Step 4: The test statistic found in step 3 has a P-value < 0.01Thus, there appears✓✓to be a useful linear relationship between y and at least one of the three predictors.Provide the linear multiple regression model with all values. (Round the intercept as well as the GPA and Pell Gran coefficients to three decimalplaces. Round the SAT coefficient to four decimal places.)2016 6-year graduation rate=Se= √ Se=GPA +Step 5: What is the value of R2 from the analysis. (Round your answer to one decimal place.)%SATWhat is the estimated random error standard deviation? (Round your answer to three decimal places.)SSResidn-(k+1)=% Pell GrantRecall that just because the model utility test performed in steps 2-4 indicates that the multiple regression model is useful, it does not automaticallyfollow that all the predictors included in the model contribute to the usefulness of the model. It is desirable that a useful model has both a large R²value and a small s value but also has relatively few predictor variables whose roles are easily interpreted. GradRate_20160.9170.8720.7240.7060.6750.8650.7640.7430.5010.9390.8320.820.7890.6450.5980.5810.3160.7010.5820.5090.4660.4340.3620.60.5480.5280.4560.3030.4130.374GPA_Avg3.873.943.793.623.623.63.643.53.183.953.753.973.693.613.323.492.933.493.53.083.373.323.23.53.23.363.263.123.43.29SAT_Med139711751271108112041303119911429021479122212111217108696010809621072106799010881069993111095111319909611029992Perc Freshmen__Pell0.230.4090.3330.2560.1990.1330.1880.1930.6320.1350.1850.0740.1450.2830.4410.4330.6790.2340.2810.4940.3190.360.4790.5120.3660.2240.5360.4880.3850.515Ratio9119191251313322111414342018651981917131519191828162313

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