Q2/The owner of a company would like to review the relationship between years on the job and weekly production units. Sample data of 5 employees revealed the table shown. A) Determine the regression equation. Years on Weekly Production job (x) units (y) B) Determine the 0.8 confidence interval for the mean weekly production for an employee worked for 7 years. 12 10 15 13 21

Q2/The owner of a company would like to review the relationship between years on the job and weekly production units. Sample data of 5 employees revealed the table shown. A) Determine the regression equation. Years on Weekly Production job (x) units (y) B) Determine the 0.8 confidence interval for the mean weekly production for an employee worked for 7 years. 12 10 15 13 21

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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10.2.13 Question Help The data show the chest size and weight of several bears. Find the regression equation, letting chest size be the independent (x) variable. Then find the best predicted weight of a bear with a chest size of 58 inches. Is the result close to the actual weight of 632 pounds? Use a significance level of 0.05. Chest size (inches) Weight (pounds) 46 57 53 41 40 40 384 580 542 358 306 320 Click the icon to view the critical values of the Pearson correlation coefficient r. What is the regression equation? y =+ x (Round to one decimal place as needed.) Enter your answer in the edit fields and then click Check Answer. 2 parts remaining Clear All Check Answer 9:31 PM P Type here to search 4/26/2021
4. A study was conducted to investigate the relationship between the size of a house (in square feet) and the selling price of a house (in dollars). The response variable is price in dollars, and we want to study if the covariate of the square footage helps explain the response. A random sample of 522 houses was used, and the linear regression output from R is below. price sqft, data = house) 1m (formula = Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -81432.946 11551.846 -7.049 5.74e-12 *** sqft 158.950 4.875 32.605 <2e-16 *** Residual standard error: 79120 on 520 degrees of freedom Multiple R-squared: 0.6715, Adjusted R-squared: 0.6709 a. Write out the estimated linear equation. What is the estimated expected selling price of a house that is 2000 square feet? b. Does the intercept have a useful interpretation in this study? Why or why not. c. Interpret the slope estimate in context of the model.
Zagat restaurant guides publish ratings of restaurants for many large cities around the world. The restaurants are rated on a 0 to 30 point scale based on quality of food, decor, service, and cost. Suppose that someone wants to predict the cost of dinner at a restaurant in a city based on the Zagat food quality ratings. If 10 restaurants in a city are sampled and the regression output is given below, what can we conclude about the slope of food quality? Since we are not given the dataset, we do not have enough information to determine if the slope differs from 0. 2) The slope is 2.521 and therefore differs from 0. 3) The slope significantly differs from 0. 4) Not enough evidence was found to conclude the slope differs significantly from 0. 5) The slope is equal to 0.
Write the formula for the estimated regression line and interpret the slope of the estimated regression line, the intercept of the estimated regression line- Is it meaningful?, and the estimated R2. Based on the fitted regression model, what is the predicted ATST for a child who is 7 years old? What is the correlation between AGE and ATST? Does the residual plot suggest that the fitted regression line is inappropriate for these data? Explain why or why not. Suppose that a new subject is added to the study data and that subject is 12.5 years old with an ATST of 580 minutes. If the regression model were to be refit with this additional data point, would the new slope be greater than or less than -14.041? Justify your response.
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Explain using evidence from graph a and graph b which graph indicates that the model for the data is a good fit .
Find the regression equation, letting overhead width be the predictor (x) variable. Find the best predicted weight of a seal if the overhead width measured from a photograph is 1.7 cm. Can the prediction be correct? What is wrong with predicting the weight in this case? Use a significance level of 0.05. Overhead Width (cm) Weight (kg) Click the icon to view the critical values of the Pearson correlation coefficient r. F n example Get more help. The regression equation is y=-159.5+ 43.2 x (Round to one decimal place as needed.) 3 # The best predicted weight for an overhead width of 1.7 cm is kg. (Round to one decimal place as needed.) I 80 E 26 D 7.8 161 C $ 4 Q F4 8.3 212 R F Stv 9.9 276 07 20 V F5 T 9.1 215 G 6 B 9.7 262 746 MacBook Air FO 7.3 166 Y H & 7 N F7 U X +00 8 J DII FB - M ( 9 K O < I ) O |qqqqqqqqi L F10 Clear all P . V : ; F11 { + [ command option Check answer = ? 11 I F12 1
Exercise 6. Regression Fallacy. Historically, scores on the two midterms had a correlation of 0.48. Suppose that Jeri scored 2.1 standard deviations below the mean on the first midterm. (a) How many standard deviations [above or below?] the mean would you predict for her second midterm?
The fish in my pond have mean length 10 inches and mean weight 4 pounds. The correlation coefficient of length and weight is .9. If the length of a particular randomly selected fish is reported to be 10 inches, then what should we predict for the weight of that fish using simple linear regression? 4 10 2.5 NOT ENOUGH INFORMATION GIVEN NONE OF THE OTHERS
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Prev The table below gives the list price and the number of bids received for five randomly selected items sold through online auctions. Using this data, consider the equation of the regression line, y = bo + b₁x, for predicting the number of bids an Item will receive based on the list price. Keep in mind, the correlation coefficient may or may not be statistically significant for the data given. Remember, In practice, It would not be appropriate to use the regression line to make a prediction if the correlation coefficient is not statistically significant. Price in Dollars 28 33 36 42 45 Number of Bids 1 7 8 9 10 Step 3 of 6: Find the estimated value of y when x = 33. Round your answer to three decimal places. Table Copy Data Next
Develop the relationship between correlation coefficient and regression coefficient. Mention the applications of correlation and regression.