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Using the appropriate output for each case study, do the following parts, and, if you have studied the optional FF test, also do the other given parts.

The Excel and Minitab outputs of a simple linear regression analysis of the data set for this case are given in Figures 13.1213.12 and 13.1313.13. Recall that labeled Excel and Minitab outputs are shown on the figure on the said page.

h Find the pp-value for testing H0:β0=0H_0: \beta_0=0 versus Ha:β00H_a: \beta_0 \neq 0 on the computer output and report its value. Using the p-value, determine whether we can reject H0H_0 by setting α\alpha equal to .10,.05,.01.10, .05, .01, and .001.001. What do you conclude about the significance of the yy intercept?

Figure 13.1213.12 Excel Output of a Simple Linear Regression Analysis of the Natural Gas Consumption Data

Regression StatisticsMultiple R0.9484R Square0.8995Adjusted R Square0.8827Standard Error0.6542Observations8ANOVAdfSSMSFSignificance FRegression122.980822.980853.69490.0003Residual62.56790.4280Total725.5488CoefficientsStandard Errort StatP-valueLower 95%Intercept15.83790.801819.75351.09E-0613.8760TEMP0.12790.01757.32770.00030.1706\begin{array}{lrrrrr} \text{Regression Statistics}\\ &&\\ \text{Multiple R} & 0.9484\\ \text{R Square} & 0.8995\\ \text{Adjusted R Square} & 0.8827\\ \text{Standard Error} & 0.6542\\ \text{Observations} & 8\\ &&\\ \text{ANOVA} & \text{df} & \text{SS} & \text{MS} & \text{F} & \text{Significance F}\\ \text{Regression} & 1 & 22.9808 & 22.9808 & 53.6949 & 0.0003\\ \text{Residual} & 6 & 2.5679 & 0.4280\\ \text{Total} & 7 & 25.5488\\ &&\\ & \text{Coefficients} & \text{Standard Error} & \text{t Stat} & \text{P-value} & \text{Lower 95\\\%}\\ \text{Intercept} & 15.8379 & 0.8018 & 19.7535 & 1.09\text{E-}06 & 13.8760\\ \text{TEMP} & -0.1279 & 0.0175 & -7.3277 & 0.0003 & -0.1706 \end{array}

Figure 13.1313.13 Minitab Output of a Simple Linear Regression Analysis of the Natural Gas Consumption Data

Analysis of VarianceSourceDFAdj MSAdj MSF-ValueP-ValueRegression122.980822.980853.690.000 Temp122.980822.980853.690.000Error62.56790.4280 Lack-of-Fit52.32290.46461.900.500 Pure Error10.24500.2450Total725.5488Model SummarySR-sqR-sq(adj)R-sq(pred)0.654208989.95%88.27%81.98%CoefficientsTermCoefSE CoefT-ValueP-ValueVIFConstant15.8380.80219.750.000Temp0.12790.01757.330.0001.00Regression EquationGasCons=15.8380.1279 TempVariableSettingFitSE Fit95% CI95% PITemp4010.72100.241483(10.1301,11.3119)(9.01462,12.4274)\begin{array}{lrrrrr} \text{Analysis of Variance}\\ \text{Source} & \text{DF} & \text{Adj MS} & \text{Adj MS} & \text{F-Value} & \text{P-Value}\\ \text{Regression} & 1 & 22.9808 & 22.9808 & 53.69 & 0.000\\ \text{ Temp} & 1 & 22.9808 & 22.9808 & 53.69 & 0.000\\ \text{Error} & 6 & 2.5679 & 0.4280\\ \text{ Lack-of-Fit} & 5 & 2.3229 & 0.4646 & 1.90 & 0.500\\ \text{ Pure Error} & 1 & 0.2450 & 0.2450\\ \text{Total} & 7 & 25.5488\\ &&\\ \text{Model Summary}\\ \text{S} & \text{R-sq} & \text{R-sq(adj)} & \text{R-sq(pred)}\\ 0.6542089 & 89.95\% & 88.27\% & 81.98\%\\ &&\\ \text{Coefficients}\\ \text{Term} & \text{Coef} &\text{SE Coef} & \text{T-Value} & \text{P-Value} & \text{VIF}\\ \text{Constant} & 15.838 & 0.802 & 19.75 & 0.000\\ \text{Temp} & -0.1279 & 0.0175 & -7.33 & 0.000 & 1.00\\ &&\\ \text{Regression Equation}\\ &&\\ \text{GasCons}=15.838-0.1279\ \text{Temp}\\ &&\\ \text{Variable} & \text{Setting} & \text{Fit} & \text{SE Fit} & \text{95\\\% CI} & \text{95\\\% PI}\\ \text{Temp} & 40 & 10.7210 & 0.241483 & (10.1301, 11.3119) & (9.01462, 12.4274) \end{array}

Question

Write a short essay explaining how the sectional conflict over slavery reflected a tension between states rights and the authority of the federal government. What was the result of this tension?

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While writing this essay, keep in mind the interests of both North and South. Try to recall and review some of the events from this period where the federal government could (or did) affect the future of slavery. Also, can you remember one event that even nullified the decision of the government?

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