Interpret the following regression outputs: coefficient estimates (including intercept), thestatistical significance of those estimates, and r²PriceSize, data =## 1m(formula#### Residuals:##Min1QMedian3Q## -107.587 -32.058 -5.829 33.000#### Coefficients:##=##‒‒‒~Estimate Std. Error t value Pr(>|t|)## (Intercept) 64.79312 38.78410 1.671 0.0978## Size0.01733 4.055 9.76e-05 ***0.07029realestate)Max113.900## Signif. codes: 0 ****' 0.001 '**' 0.01 '*' 0.05. 0.1#### Residual standard error: 43.95 on 103 degrees of freedomI1## Multiple R-squared: 0.1377, Adjusted R-squared: 0.1293## F-statistic: 16.44 on 1 and 103 DF, p-value: 9.756e-05

From the given regression output, the regression model is as follows.

Answered: Interpret the following regression…

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

See similar textbooks

Similar questions

A linear regression model is designed to predict service charges by a bank (in dollars per month) based on sales revenues of 26 companies who use the services (in millions of dollars). Partial output from Excel gives ŷ = -5428 + 32.756x,, with SSE = 117600, and the p-value = 0.04022 for the estimated slope. Interpret the standard error of the estimate. A B E Approximately 95% of the observed service charges fall within $117600 of the least squares line None of the suggested answers are correct For every $1 million increase in sales revenue, we expect a service charge to increase by $117600 Approximately 96% of the observed service charges fall within $140 of the least squares line. Approximately 95% of the observed service charges equal their corresponding predicted values
An economist is interested to see how consumption for an economy (in $ billions) is influenced by gross domestic product ($ billions) and aggregate price (consumer price index). The Minitab output of this regression is reproduced below. Coefficients Term Constant GDP Price Model Summary S 0.299 Error Total Coef Analysis of Variance Source Regression -0.0861 0.7654 -0.0006 R-sq 0.98% DF • 2 7 9 SE Coef 0.5644 0.0574 0.0028 R-sq(adj) 0.98% Adj SS 33.4163 0.6277 34.0440 T-Value O a. 7 O b. 9 O c. 10 O d. Cannot be determined from the information given. -0.1520 13.3400 -0.1290 Adj MS 16.7082 0.0897 P-Value 0.8837 0.0001 0.8330 F-Value P-Value 186.325 0.049 Referring to the table above, how many observations were there in this study?
The attached images show linear regression analysis to evaluate the ability of independent variables full and part-time FTEs, number of Medicare certified beds and urban vs. rural setting to predict dependent variable, occupancy rate. How do you interpret these results, what are the basic assumptions for regression analysis?
The following data gives the number of employees at the bookstore and the number of minutes students wait in line to buy books at the beginning of the term. The independent variable is the number of employees and the dependent variable is the number of minutes. What is the y intercept? SSxx = 56.857; SS=2095.714; SSxy=-322.571 SUMMARY OUTPUT Regression Statistics Multiple R R Square Adjusted R Square XA356899 0.96 0.93 0.91 y 67 54 47 33 31 25 12
Listed below are the overhead widths (cm) of seals measured from photographs and weights (kg) of the seals. Find the regression equation, letting the overhead width be the predictor (x) variable. Find the best predicted weight of a seal if the overhead width measured from a photograph is 1.8 cm, using the regression equation. Can the prediction be correct? If not, what is wrong? Use a significance level of 0.05. Overhead Width (cm) 7.3 7.4 9.8 9.5 8.8 8.5 Weight (kg) 152 187 286 247 237 231 The regression equation is y =+ (x. (Round the y-intercept to the nearest integer as needed. Round the slope to one decimal place as needed.)
Why this type of plot is used in analysing regression models
please solve all parts as soon as possible
Develop a scatterplot and explore the correlation between customer age and net sales by each type of customer (regular/promotion). Use the horizontal axis for the customer age to graph. Find the linear regression line that models the data by each type of customer. Round the rate of changes (slopes) to two decimal places and interpret them in terms of the relation between the change in age and the change in net sales. What can you conclude? Hint: Rate of Change = Vertical Change / Horizontal Change = Change in y / Change in x
Question. Using the following regression result, make your answers to the questions. Call: Im(formula - wage - educ, data - data) Residuals: 10 Median -5.1579 -2.5066 0.3816 2.4539 4.4737 Min 30 Max Coefficients: Estimate Std. Error t value Pr(>1tl) (Intercept) 9.0526 educ 5.1420 1.761 0.11635 2.1842 0.5994 3.644 0.00655 ** --- Signif. codes: 0 ***** 0.001 *** 0.01 ** 0.05 . 0.1' 1 Residual standard error: 3.305 on 8 degrees of freedom Multiple R-squared: 0.6241, F-statistic: 13.28 on 1 and 8 DF, p-value: 0.006549 Adjusted R-squared: 0.5771 1) Using the estimate and standard error of educ variable, perform the following hypothesis test. Ho:Beduc = 0 H1: Beduc # 0 2) Interpret the above hypothesis test result.
A regression of infant mortality rates (annual deaths per hundred births) on median annual household income (in thousands of dollars) is performed on data collected from 40 counties. The results are summarized as follows. Intercept: Slope: 18.46 -0.14 Sum of df Mean Squares Square Regression Residual Total 2.4 4.5 (a) Fill in the blanks in the ANOVA table (b) What is the predicted infant mortality rate in a county where the median household income is $40,000? (c) In words, what is the meaning of the coefficient -0.14? Do NOT simply say that this is the slope or the regression coefficient. Indicate what it means and how it can be interpreted.
Please do only the last three
i submitted a picture of the question

SEE MORE QUESTIONS

Recommended textbooks for you

MATLAB: An Introduction with Applications

Statistics

ISBN:

9781119256830

Author:

Amos Gilat

Publisher:

John Wiley & Sons Inc

Probability and Statistics for Engineering and th…

Statistics

ISBN:

9781305251809

Author:

Jay L. Devore

Publisher:

Cengage Learning

Statistics for The Behavioral Sciences (MindTap C…

Statistics

ISBN:

9781305504912

Author:

Frederick J Gravetter, Larry B. Wallnau

Publisher:

Cengage Learning

MATLAB: An Introduction with Applications

Statistics

ISBN:

9781119256830

Author:

Amos Gilat

Publisher:

John Wiley & Sons Inc

Probability and Statistics for Engineering and th…

Statistics

ISBN:

9781305251809

Author:

Jay L. Devore

Publisher:

Cengage Learning

Statistics for The Behavioral Sciences (MindTap C…

Statistics

ISBN:

9781305504912

Author:

Frederick J Gravetter, Larry B. Wallnau

Publisher:

Cengage Learning

Elementary Statistics: Picturing the World (7th E…

Statistics

ISBN:

9780134683416

Author:

Ron Larson, Betsy Farber

Publisher:

PEARSON

The Basic Practice of Statistics

Statistics

ISBN:

9781319042578

Author:

David S. Moore, William I. Notz, Michael A. Fligner

Publisher:

W. H. Freeman

Introduction to the Practice of Statistics

Statistics

ISBN:

9781319013387

Author:

David S. Moore, George P. McCabe, Bruce A. Craig

Publisher:

W. H. Freeman

SEE MORE TEXTBOOKS