A study on the sales (Y=no. of units sold) and the price (X, in pesos) showed the following result:SUMMARY OUTPUTRegression StatisticsMultiple RR SquareAdjusted R SquareStandard ErrorObservationsANOVARegressionResidualTotalInterceptPrice0.77270.59710.8706265781.086529053df20CoefficientsSSMSFSignificance F1 152.1271 152.1271 128.8617 1.2273E-0918 21.24982 1.18054519 173.3769SEt Stat P-value Lower 95% L2259 1.593473 46.61723 3.17E-20 70.93555091.316758 11.35173 1.23E-09 12.1810733-1418According to this model, how many units will be sold, on the average, when the price of the item is Php1.10?699.21066.93902.93818.8

The following information has been provided: Let X=PRICE is an independent variable Y=NO. OF UNITS…

Answered: A study on the sales (Y=no. of units…

A First Course in Probability (10th Edition)

10th Edition

ISBN:9780134753119

Author:Sheldon Ross

Publisher:Sheldon Ross

Chapter1: Combinatorial Analysis

Section: Chapter Questions

Problem 1.1P: a. How many different 7-place license plates are possible if the first 2 places are for letters and...

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Simple regression was employed to establish the effects of childhood exposure to lead. The effective sample size was about 122 subjects. The independent variable was the level of dentin lead (parts per million). Below are regressions using various dependent variables. (a) Calculate the t statistic for each slope, at significance level = 0.01. (Negative values should be indicated by a minus sign. Round your answers to 2 decimal places.) Dependent Variable R2 Estimated Slope Std. Error tcalculated p-value Differ from 0? Highest grade achieved .061 −0.027 0.009 .008 (Click to select) Yes No Reading grade equivalent .121 −0.070 0.018 .000 (Click to select) Yes No Class standing .039 −0.006 0.003 .048 (Click to select) No Yes Absence from school .071 4.8 1.7 .006 (Click to select) No Yes Grammatical reasoning .051 0.159 0.062 .012 (Click to select) No…
3. The following tables show the result of statistical analysis in a study titled "Correlates of Students Performance in Intermediate Algebra". :d. What is the F-value? e. Is the overall regression significant? Justify your answer f. Using APA format, make a writeup on the result of the analysis Table 1. Summary Adjusted R Square Std. Error of the Estimate R Square 55226 305 470 3.081 Table 2. ANOVA Sum of Squares Sig- Df Mean Square Regression Residual 2904.729 15 193.649 20.396 .001" 2971.696 313 9.494 Total 5876.426 328 Table 3. Coefficients Significance Predictors MATH_GPA_1styear 0.379 0.012 AGE 0.433 0.009 SEX 0.497 0.022 MOTHER EDUCATIONAL ATTAINMENT 0.021 0.080 FATHER EDUCATIONAL ATTAINMENT 0.069 0.070 INCOME OF MOTHER 0.088 0.634 INCOME OF FATHER 0.466 0.019 NUMBER OF BROTHERS 0.025 0.820 NUMBER OF SISTERS 0.134 0.216 NUMBER OF BOOKS 0.352 0.007 AVAILABILITY OF COMPUTER 1.757 0.019 ACCESS TO INTERNET 1.353 0.000 STUDY HOURS DAILY 1.229 0.001 ASSISTANCE OF OTHER PEOPLE 1.412…
Using the following information. Coefficients Intercept -12.8094 Independent variable 2.1794 ANOVA df SS MS F Regression 1 12,323.56 12,323.56 90.0481 Residual 8. 1,094.842 136.8553 Total 9. 13,418.4 Estimate the value of Ý when X= 4.
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The regression equation for predicting a woman's muscle mass (a quantitative metric, Y) from age (in years, X) is given as ý = 156.35 - 1.19. The ANOVA table for the linear regression of Y vs X is given below. Source Df SS MS p-value Age 11627.5 11627,5 174.06 <,0001 Error 58 3874.4 66.8 Total 59 15501.9 Fill in the blanks below that demonstrate how to construct an interval estimate for the muscle mass of an individual 60-year-old woman. Use a confidence level of 95%; note that the multiplier is t = 2.002 and the standard error of the mean response is s(i,) = 1.055. 95% Interval: ( Select) + ( Select ) [ Select]
Dex Research Limited conducted a research to investigate consumer characteristics that can be used to predict the amount charged by credit card users. The following multiple regression output is based on a data collected by this research company on annual income, household size and annual credit card charges for a sample if 50 consumers. Regression Statistics Multiple R R Square Adjusted R Square Standard Error 0.9086 A 0.8181 398.0910 Observations ANOVA df SS MS Significance F Regression 2 1.50876E-18 Residual C 7448393.148 F Total 49 42699148.82 Coefficients Standard Error t Stat P-value Intercept 1304.9048 197.6548 6.6019 3.28664E-08 Income ($1000s) 33.1330 3.9679 H 7.68206E-11 Household Size 356.2959 33.2009 10.7315 3.12342E-14 a. Complete the missing entries from A to H in this output b. Estimate the annual credit card charges for a three-person household with an annual income of $40,000. C. Did the estimated regression equation provide a good fit to the data? Explain
The summary output obtained from fitting the multiple regression are given below. Model Unstandardized Coefficients Standardized Sig. Coefficients B Std. Error Beta -3.512 (Constant) Education (years) -3019.226 859.789 .000 658.518 45.852 .581 14.362 .000 Gender -1615.440 253.239 -.249 -6.379 .000 Age (years) 45.008 10.469 .163 4.299 .000 Dependent Variable: Beginning Salary, Male=0 & Female=1. (a) Write down the estimated multiple regression model of the beginning salary on education, gender and age of employees of a company. (b) Interpret estimated regression coefficient values. (c) Find the predicted beginning salary for an employee who is 24 years old male and has 17 years of education.
A sales manager for an advertising agency believes there is a relationship between the number of contacts that a salesperson makes and the amount of sales dollars earned. A regression analysis shows the following results. Coefficients Standard Error t-Stat p-value Intercept -12.201 6.560 -1.860 0.100 Number of contacts 2.195 0.176 12.505 0.000 ANOVA df SS MS F Significance F Regression 1.00 |13,555.42 |13,555.42 156.38 0.00 Residual 8.00 693.48 86.68 Total 9.00 14,248.90 Assume that X = 33.4 and E(X – X) 2814.4. Rounding to one decimal place, the 95% confidence interval for 30 calls is
A business is evaluating their advertising budget, and wishes to determine the relationship between advertising dollars spent and changes in revenue. Below is the output from their regression. SUMMARY OUTPUT Regression Statistics Multiple R 0.95 R Square 0.90 Adjusted R Square 0.82 Standard Error 0.82 Observations 8 ANOVA df SS MS F Significance F Regression 3 23.188 7.729 11.505 0.020 Residual 4 2.687 0.672 Total 7 25.875 Coefficients Std Error t Stat P-value Lower 95% Upper 95% Intercept 83.91 2.03 41.36 0.00 78.28 89.54 TV ($k) 1.96 0.48 4.10 0.01…
Please help interpret this SPSS output of a multiple regression analysis result? DV- To what extent the product safety incident have changed your perceptions of the involved brand(s) IV'S- Trustworthy , Reliability , I don’t know how good the company’s products will be before I buy it. , Preventability
Why this type of plot is used in analysing regression models
Waterbury Insurance Company wants to study the relationship between the amount of fire damage and the distance between the burning house and the neorest fire station. This information will be used in setting rates for insurance coverage. For o somple of 30 claims for the last year, the director of the actuarial department determined the distance from the fire station (x) and the amount of fire domage, in thousands of dollars (y). ANOVA table Source Regression Residual SS df MS F 1,870.5782 1,870.5782 41.39 1,265.4934 3,136.0716 28 45.1962 Total 29 Regression output Standard Variables Coefficients Error t(df-28) Intercept Distance-X 13.76815 3.106 2.914 3.77es e. 5861 6.43 Click here for the Excel Data File a-1. Determine the regression equation. (Round your answers to 3 decimal places.) y3D X. a-2 Is there a direct or indirect relotionship between the distance from the fire station and the amount of fire damage? The relationship between distance and damage is b. How much domage would…

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