### Multiple Regression Analysis Task#### Given Information:- Sample size ($n$) = 20- Slope for variable $X_1$ ($b_1$) = 10- Slope for variable $X_2$ ($b_2$) = 35- Standard error for $b_1$ ($S_{b_1}$) = 6- Standard error for $b_2$ ($S_{b_2}$) = 10#### Instruction A:**Question:** Which variable has the largest slope, in units of a t statistic?- **Correct Answer:** $X_2$ - Explanation: The t statistic is calculated as the slope divided by its standard error. For $X_1$, this is $10/6 \approx 1.67$. For $X_2$, it is $35/10 = 3.5$. Therefore, $X_2$ has the largest t statistic.#### Instruction B:**Question:** Construct a 95% confidence interval estimate of the population slope, $\beta_1$.\[ \text{Confidence Interval: } [ \, \_\_\_ \leq \beta_1 \leq \_\_\_ \, ] \]- **Note:** Remember to round to four decimal places as needed. Use the t-distribution with $n - 2$ degrees of freedom to find the critical value, then calculate the interval using $b_1 \pm t \times S_{b_1}$.This presentation ensures a clear understanding of the problem, its context, and the step-by-step approach to find the solution.

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MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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. A professor at the University of Alabama was interested in evaluating the relationship between family support and delinquency. Using data collected on 4545 families, the researcher used regression to analyze the relationship. The results are presented below. Variables Entered/Removeda Model Variables Entered Variables Removed Method 1 Family supportb . Enter a. Dependent Variable: Delinquency b. All requested variables entered. Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate 1 .249a .062 .062 1.59168 a. Predictors: (Constant), Family support ANOVAa Model Sum of Squares df Mean Square F Sig. 1 Regression 759.204 1 759.204 299.671 <.001b Residual 11479.107 4531 2.533 Total 12238.311 4532 a. Dependent Variable: Delinquency b. Predictors: (Constant), Family support…
The station manager of a local television station is interested in predicting the amount of television (in hours) that a person in the viewing area will watch. The explanatory variables are age (in years), education (highest level obtained, in years) and family size (number of people in household). The multiple regression output is shown below: Summary measures Multiple R R-Square Adj R-Square 0.6644 StErr of Estimate 0.5598 ANOVA Table Source df SS MS F p-value Explained 3 13.9682 4.6561 0.0000 Unexplained 18 5.6413 0.3134 Regression coefficients Coefficient Std Err t-value p-value Constant 1.683 1.1696 1.4389 0.1674 Age -0.0498 0.0199 -2.5018 0.0222 Education 0.2135 0.0503…
Fifty matched pairs of magnitude/depth measurements randomly selected from 10,594 earthquakes recorded in one year from a location in southern California. Find the best predicted depth of an earthquake with a magnitude of 2.2. Use a significance level of 0.05. : Click the icon to view the earthquake measurements. Click here to view the critical values of the Pearson correlation coefficient, r. The regression equation is y = ? + ?x. 回-ト i Critical Values of the Pearson Correlation Coefficient r (Round to one decimal place as needed.) Critical Values of the Pearson Correlation Coefficient r α= 0.05 0.950 0.878 0.811 0.754 0.707 0.666 0.632 0.602 0.576 a = 0.01 0.990 0.959 0.917 0.875 0.834 0.798 0.765 0.735 0.708 NOTE: To test Ho: p=0 Jagainst H,: p#0, reject H, if the absolute value of r is greater than the critical value in the table. 5 10 11 12
Based on the sample data set: (0,0) ( 2,3) (3,3) (6,4) (9,8) A. Construct the 90% confidence interval for the slope B1 of the population regression line B. find the Coefficient of determination using the formula r2=B1SSxy / SSyy.
Construct a 95 % confidence interval for the slope of the regression line. Round your answers to two decimal places, if necessary. Heights of Fathers and Sons (in Inches) Height of Father, x Height of Son, y 71 71 74 75 67 67 68 72 68 70 75 75 70 70 72 71 72 72 Copy Data E Tables E Keypad Answer Keyboard Shortcuts Lower endpoint: Unner endnoint:
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The following regression model was fitted to sample data with 12 observations: = 30 +4.50x. What is the residual for an observation (x = 2, y = 40)? 0.50 -1 1 -0.50
Consider the following computer output from a multiple regression analysis relating the cost of car insurance to the variables: number of car accidents, driver's credit score, and safety rating of the car. Intercept Car Accidents (In last 3 years) Credit Score Safety Rating Coefficients 1186 213.48 Coefficients - 130.46 294.11 Standard Error Does the sign of the coefficient for the variable safety rating make sense? 123.87 21.89 14.26 356.37 t Stat P-value 9.575 0.0000 9.752 0.0000 -9.149 0.0000 0.825 0.4128
Below are the results of Multiple Regression Analysis. Write down the interpretation of the following tables: R Model R Square .560a .314 Change Statistics F Change df1df2 1 .314 7.210 9 142 a. Predictors: (Constant), Horsepower, Wheelbase, Fuel efficiency, Width, Price in thousands, Fuel capacity, Length, Engine size, Curb weight b. Dependent Variable: Sales in thousands Adjusted R Square Model Summaryb .270 Std. Error of the Estimate 58.895390 R Square Change Sig. F Change .000
Data on the price ($1000s) and the size (1000 ft2 sqaured) for 156 randomly selected houses that have been sold is summarized in the accompanying regression output and plots. Complete parts a and b a) Check the assumptions and conditions for inference. Select all that apply. A. The residuals look random. B. The residuals are nearly normal. C. The scatterplot looks straight enough. D. The residual plot and the scatterplot show consistent variability. E. None of the assumptions and conditions for inference are satisfied.
Please assist second part? Thank you!

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