**Understanding the Relationship Between Homework and Final Grades**Students at a local university believe that their statistics instructor assigns extensive homework to penalize them. They wonder if there is a relationship between the number of optional homework problems they complete during the semester and their final grades. The students decide to track the number of these problems completed throughout the semester, recording each student's total and their final grade at the end.**Research Question:**What is the regression equation to predict the final grade from the number of homework assignments completed?**Data Table:**| Problems (X) | Grade (Y) | (X-Ō) | (Y-Ȳ) | (X-Ō)(Y-Ȳ) | (X-Ō)² | (Y-Ȳ)² ||--------------|----------|-------|-------|------------|--------|--------|| 51 | 62 | -21.75| -8.67 | 188.57 | 473.06 | 75.17 || 58 | 68 | -14.75| -2.70 | 39.83 | 217.56 | 7.29 || 62 | 66 | -10.75| -4.67 | 50.20 | 115.56 | 21.81 || 65 | 66 | -7.75 | -4.67 | 36.19 | 60.06 | 21.81 || 68 | 67 | -4.75 | -3.67 | 17.43 | 22.56 | 13.47 || 76 | 72 | 3.25 | 1.33 | 4.32 | 10.56 | 1.77 || 77 | 73 | 4.25 | 2.33 | 9.90 | 18.06 | 5.43 || 78 | 72 | 5.25 | 1.33 | 6.98 | 27.56 | 1.77 || 78 | 78 | 5.25 | 7.33 | 38.48

The given data represents that the final grade and the number of problems assigned for homework.

Students at a local university believe that their statistics instructor gives them homework assignments just to punish them. They wonder if there is any relationship between the number of optional homework problems they do during the semester and their final course grade. They decide to keep track of the number of these problems completed during the course of the semester. At the end of the class each student's total is recorded along with their final grade. What is the regression equation to predict the final grade from the number of homework assignments completed? Problems Grade (X-X) (Y-Y) (X-X)(Y-Y) (X-X)? (Y-Y)² Y 51 62 -21.75 -8.67 188.57 473.06 75.17 58 68 -14.75 -2.7 39.83 217.56 7.29 62 66 -10.75 -4.67 50.20 115.56 21.81 65 66 -7.75 -4.67 36.19 60.06 21.81 68 67 -4.75 -3.67 17.43 22.56 13.47 76 72 3.25 1.33 4.32 10.56 1.77 77 73 4.25 2.33 9.90 18.06 5.43 78 72 5.25 1.33 6.98 27.56 1.77 78 78 5.25 7.33 38.48 27.56 53.73 84 73 11.25 2.33 26.21 126.56 5.43 85 76 12.25 5.33 65.29 150.06 28.41 91 75 18.25 4.33 79.02 333.06 18.75 873 848 562.44 1582.25 254.83

Students at a local university believe that their statistics instructor gives them homework assignments just to punish them. They wonder if there is any relationship between the number of optional homework problems they do during the semester and their final course grade. They decide to keep track of the number of these problems completed during the course of the semester. At the end of the class each student's total is recorded along with their final grade. What is the regression equation to predict the final grade from the number of homework assignments completed? Problems Grade (X-X) (Y-Y) (X-X)(Y-Y) (X-X)? (Y-Y)² Y 51 62 -21.75 -8.67 188.57 473.06 75.17 58 68 -14.75 -2.7 39.83 217.56 7.29 62 66 -10.75 -4.67 50.20 115.56 21.81 65 66 -7.75 -4.67 36.19 60.06 21.81 68 67 -4.75 -3.67 17.43 22.56 13.47 76 72 3.25 1.33 4.32 10.56 1.77 77 73 4.25 2.33 9.90 18.06 5.43 78 72 5.25 1.33 6.98 27.56 1.77 78 78 5.25 7.33 38.48 27.56 53.73 84 73 11.25 2.33 26.21 126.56 5.43 85 76 12.25 5.33 65.29 150.06 28.41 91 75 18.25 4.33 79.02 333.06 18.75 873 848 562.44 1582.25 254.83

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 county real estate appraiser wants to develop a statistical model to predict the appraised value of houses in a section of the county called East Meadow. One of the many variables thought to be an important predictor of appraised value is the number of rooms in the house. Consequently, the appraiser decided to fit the simple linear regression model, y = b₁x + bowhere y = appraised value of the house (in $thousands) and x = number of rooms. Using data collected for a sample of n=74 houses in East Meadow, the following results were obtained: y=74.80+ 17.80x Give a practical interpretation of the estimate of the slope of the least squares line. For each additional room in the house, we estimate the appraised value to increase $74,800. 1000 For each additional dollar of appraised value, we estimate the number of rooms in the house to increase by 17.80 rooms. For a house with 0 rooms, we estimate the appraised value to be $74,800. For each additional room in the house, we estimate the…
Using your favorite statistics software package, you generate a scatter plot with a regression equation and correlation coefficient. The regression equation is reported asy=−71.07x+16.92y=-71.07x+16.92and the r=−0.52r=-0.52.What percentage of the variation in y can be explained by the variation in the values of x?r² = % (Report exact answer, and do not enter the % sign)
During the 1950's and 1960's the average weight of vehicles sold in the U.S. was well over 4,000 pounds. There was a dip in the average weights in the 1970's and 1980's, due possibly to both higher demand for better gas mileage and a world-wide shortage of crude oil. Then in the 1990's and early 2000's the average weight of vehicles had a steady increase. A regression analysis was completed on the average weight of the 10 most commonly sold vehicles in the U.S. from the years 2012 through the year 2020 and yielded the following results, where the independent variable is the year and the predicted variable is the average weight of the 10 most popular vehicles. Correlation of "Average Weight" and "Year" = r = 0.9283 The regression equation is "Average Weight" = –124,960.73 + 63.82(Year) Predict the Average Weight to the nearest pound for the 2022 Year. Group of answer choices A. 4057 pounds B. 4015 pounds C. This value of Year is beyond the scope of the…
3. Describe the problem that outliers present for a regression analysis and outline what you could do to resolve this problem.
A researcher predicts that gender (male and female) and race (race 1, race 2 and race 3) are factors which determine the willingness of a person to get involved in politics. He plan to use a multiple regression test to analyse his data. Is the multiple regression test appropriate for the study? Elaborate on your answer.
Consider the following passage: I ran a regression, with many variables to predict the result of another variable which was the murder rate. One can see that lots of things, can cause the murder rate to increase or decrease. I tried to account for all the important factors, and those factors are the SAT scores, unemployment rate, and international migration per 1,000. The SAT score is, average combined total score participants did on the SAT exam. The unemployment rate is, "a measure of the prevalence of unemployment and it is calculated as a percentage by dividing the number of unemployed individuals by all individuals currently in the labor force." (Wikipedia) International migration per 1,000 is, the number of people who come into a state from other countries per 1,000 people who live in the state. After I run the regression I will look at the t scores and p values and I should hopefully conclude that international migration does not cause crime. Which writing mistakes, if any, did…
Help with my homework
What are the coefficients of correlation between miles per gallon and horsepower? Between miles per gallon and the weight of the car? What are the directions and strengths of these coefficients? Do the coefficients of correlation indicate a strong correlation, weak correlation, or no correlation between these variables? See Step 4 in the Python script. Write the multiple regression equation for miles per gallon as the response variable. Use weight and horsepower as predictor variables. See Step 5 in the Python script. How might the car rental company use this model?
Identify whether this statement is True or False and provide justification for your answer. “A positive correlation between attitude and intention will always result in a significant effect between the two variables in regression analysis.”
If the equation of the regression line that relates income in dollars of student’s parents, xx, with cost in dollars of tuition,yy, is y=8500+0.018xy=8500+0.018x. a. What is the best prediction for the tuition of students whose parents earn no income? b. What is the best prediction for the tuition of students whose parents earn $45000? c. What is the best prediction for the tuition of students whose parents earn $80000?
When you are deciding which variables to include as predictors in a multiple regression equation, what are some conditions that you must consider first?
Melinda thinks that stress might predict lower grades on a statistics final exam. She gives a sample of statistics students a questionaire and then looks for relationships between their answers and their final exam scores. Although she finds no support for her original hypothesis, she does find that the the numbers of hours that students work outside of school is related to exam performance. When she reports the results of her study, she presents them as if the relationship between number of hours worked and exam performance was her original hypothesis. This is a questionable research practice known as: a. open science b. p-hacking c. HARKing d. constraints of generality