Given the data provided, I need help solving parts C-E of my problem. Thank you in advance! Causeeffect5.a. What is the predictor (X) variable and what is the response (Y) variable?X-basal heart sateY- temperatureDo the data meet the assumptions for linear regression? Explain with reference to thestudy description and specific output that support your statements.b.RUs vresiduals are normaleach animal randomly assigred.Association is inearNo outlierssame size, age, gendry tomp targeAssumptions are FuifFiledWrite the regression equation for this relationship (i.e., include the actual slope andintercept coefficient values, and use the variable names Temp and BHR).C.d.Use the regression equation to compute the predicted value for heart rate (ỳ) and theresidual for each of the sub-set of the following temperature (X) values:Frog ID#139.11Temperature9.15212733BHR (beats/min)31420283342Predicted BHRResiduale. The first line of the regression Coefficients: table that you generated presentsresults for (Intercept), including the estimate and results of a t-test for the Nullhypothesis that the y-intercept is 0.(1) What is the biological interpretation of (Intercept) =-0.236?(2) Explain the meaning of the p-value from the associated t-test.13.6 5. A student in an animal physiology class did an experiment to determine the effect ofenvironmental temperature on the heart rate of leopard frogs. She obtained 11 frogs ofapproximately the same age, size, and gender, and randomly assigned each animal to acontainer kept at a temperature between 3 and 33°C. After the frogs had equilibrated to theirambient temperature, she measured their basal heart rate (BHR).The output below displays the results of exploratory data analysis and linear regressionanalysis of the association between basal heart rate (BHR) and Temperature (Temp). Notethat the coefficients Intercept and Temp refer to the y-intercept and slope of the regressionequation, respectively.Shapiro-Wilk normality testdata:residuals (1m (BHR ~ Temp, data))W = 0.98973, p-value = 0.9972%3DResiduals vs FittedNormal Q-QResiduals vs Leverage2.0330030.504200.580OgY --- Cook's distance0810203040-1.50.50.51.50,000.100.200.30Fitted valuesIm(BHR - Temp)Theoretical QuantilesIm(BHR - Temp)LeverageIm(BHR - Temp)Plot of BHR vs TempCall:40lm (formula = BHR ~ Temp, data = data)Residuals:10 Median-3.0182 -1.0636 0.4182Min3QМаx200.9818 2.8909Coefficients:Estimate Std. Error t value Pr(|t|)1.(Intercept) -0.23636Temp1.17059-0.2020.8445.1015 20 25 301.260610.05753 21.912 4.05e-09 ***TempSignif. codes: 0 '***' 0.001 I*** 0.01 I*! 0.05 '.' 0.1 ' ' 1Residual standard error: 1.81 on 9 degrees of freedomMultiple R-squared: 0.9816, Adjusted R-squared: 0.9796F-statistic: 480.1 on 1 and 9 DF,p-value: 4.053e-0913.5Residuals-3 -2 -1 0 1 2 3Standardized residualsStandardized residualsO L-

Question

Given the data provided, I need help solving parts C-E of my problem. Thank you in advance! Causeeffect5.a. What is the predictor (X) variable and what is the response (Y) variable?X-basal heart sateY- temperatureDo the data meet the assumptions for linear regression? Explain with reference to thestudy description and specific output that support your statements.b.RUs vresiduals are normaleach animal randomly assigred.Association is inearNo outlierssame size, age, gendry tomp targeAssumptions are FuifFiledWrite the regression equation for this relationship (i.e., include the actual slope andintercept coefficient values, and use the variable names Temp and BHR).C.d.Use the regression equation to compute the predicted value for heart rate (ỳ) and theresidual for each of the sub-set of the following temperature (X) values:Frog ID#139.11Temperature9.15212733BHR (beats/min)31420283342Predicted BHRResiduale. The first line of the regression Coefficients: table that you generated presentsresults for (Intercept), including the estimate and results of a t-test for the Nullhypothesis that the y-intercept is 0.(1) What is the biological interpretation of (Intercept) =-0.236?(2) Explain the meaning of the p-value from the associated t-test.13.6 5. A student in an animal physiology class did an experiment to determine the effect ofenvironmental temperature on the heart rate of leopard frogs. She obtained 11 frogs ofapproximately the same age, size, and gender, and randomly assigned each animal to acontainer kept at a temperature between 3 and 33°C. After the frogs had equilibrated to theirambient temperature, she measured their basal heart rate (BHR).The output below displays the results of exploratory data analysis and linear regressionanalysis of the association between basal heart rate (BHR) and Temperature (Temp). Notethat the coefficients Intercept and Temp refer to the y-intercept and slope of the regressionequation, respectively.Shapiro-Wilk normality testdata:residuals (1m (BHR ~ Temp, data))W = 0.98973, p-value = 0.9972%3DResiduals vs FittedNormal Q-QResiduals vs Leverage2.0330030.504200.580OgY --- Cook's distance0810203040-1.50.50.51.50,000.100.200.30Fitted valuesIm(BHR - Temp)Theoretical QuantilesIm(BHR - Temp)LeverageIm(BHR - Temp)Plot of BHR vs TempCall:40lm (formula = BHR ~ Temp, data = data)Residuals:10 Median-3.0182 -1.0636 0.4182Min3QМаx200.9818 2.8909Coefficients:Estimate Std. Error t value Pr(>|t|)1.(Intercept) -0.23636Temp1.17059-0.2020.8445.1015 20 25 301.260610.05753 21.912 4.05e-09 ***TempSignif. codes: 0 '***' 0.001 I*** 0.01 I*! 0.05 '.' 0.1 ' ' 1Residual standard error: 1.81 on 9 degrees of freedomMultiple R-squared: 0.9816, Adjusted R-squared: 0.9796F-statistic: 480.1 on 1 and 9 DF,p-value: 4.053e-0913.5Residuals-3 -2 -1 0 1 2 3Standardized residualsStandardized residualsO L-

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Given information:
The output of simple linear regression model is given.