**Research on Mid-Term Exam, Study Hours, and Final Grades in QMB 3600****Data Table:**A study in the QMB 3600 course analyzed the relationship among mid-term exam grades, average weekly study hours, and final course grades. Data collected from a previous semester are presented in the table below:| Student # | Mid-Term Exam Grade | Average Number of Study Hours per Week | Final Course Grade ||-----------|---------------------|----------------------------------------|--------------------|| 1 | 50.0 | 2.0 | 65.0 || 2 | 60.0 | 4.0 | 85.0 || 3 | 55.0 | 3.5 | 78.0 || 4 | 85.0 | 6.0 | 90.0 || 5 | 55.0 | 5.0 | 70.0 || 6 | 72.0 | 4.5 | 89.0 || 7 | 75.0 | 6.5 | 91.0 || 8 | 45.0 | 3.0 | 65.0 || 9 | 88.0 | 5.5 | 89.0 || 10 | 90.0 | 7.5 | 96.0 |**Tasks:**a) **Linear Regression Model (Mid-Term Exam):** - Develop a linear regression model where Y (dependent variable) is the Final Course Grade and X (independent variable) is the Mid-Term Exam Grade. - Calculate the coefficient of determination ($r^2$), the coefficient of correlation (r), and the model variance ($\sigma^2$). - Conduct hypothesis testing to assess the model's significance at $\alpha = 0.05$. Follow the steps outlined in the textbook's Triple A Construction example on page 121.b) **Scatter Diagram (Mid-Term Exam):** - Plot a scatter diagram using data from task (a). - Add the linear regression model to the diagram for visual correlation assessment.c) **Linear Regression Model (Study Hours): **Title: Analyzing Sales Data of Air Conditioning Units**---**Introduction:**An air conditioning company located in Central Florida collected data on the number of air conditioning units sold and the outside temperature on the day of sales. The Sales Manager compiled the following data:---**Table: Outside Temperature and Sales Data**| Outside Temperature (Degrees F) | Number of Air Conditioning Units Sold ||---------------------------------|--------------------------------------|| 68 | 3 || 72 | 5 || 78 | 7 || 81 | 12 || 84 | 15 || 86 | 16 || 89 | 22 || 91 | 18 || 93 | 19 || 94 | 26 |---**Tasks:****a) Scatter Diagram:**- Plot a scatter diagram for the data provided above.- Draw the linear regression line calculated in topic (b).- Variables: - Y: Number of air conditioning units sold - X: Outside temperature (degrees F)- Guidance: The graph should resemble Figure 4.2 in the textbook.**b) Linear Regression Calculation:**- Perform a linear regression calculation to provide the equation describing the relationship between Y (units sold) and X (temperature).- Guidance: Follow the steps in Table 4.2 of the textbook.**c) SST, SSE, and SSR Calculation:**- Calculate the SST (Total Sum of Squares), SSE (Sum of Squares for Error), and SSR (Sum of Squares for Regression) for this linear regression.- Guidance: Follow the steps in Table 4.3 of the textbook.**d) Coefficient of Determination and Correlation:**- Calculate the coefficient of determination ($ r^2 $) and the coefficient of correlation (r).**e) Estimation and Error Analysis:**- Use the linear regression equation from topic (b) to estimate sales for days with temperatures of 84°F and 94°F.- For both temperatures, calculate the error "e" by comparing estimated sales against actual data.- Discuss at which temperature the model is more accurate, with explanations.---**Conclusion:**This exercise aims to enhance understanding of linear regression application in real-world scenarios, focusing on how outside temperature impacts air conditioning sales.---

Hello! As you have posted more than 3 sub parts, we are answering the first 3 sub-partsr2, r, σ2.…

Answered: Students in QMB 3600 – Quantitative…

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

Answer the following questions. 1. Explain the terms 'classification and tabulation' and point out their importance in a statistical study. Comment on, 'Tabular presentation does not add anything to the information already contained in the data'.
The table below shows the number of state-registered automatic weapons and the murder rate for several states in the U.S. 11.6 8.4 6.7 3.5 2.7 2.7 2.2 0.9 14.1 11.3 9.9 6.8 6.2 6.3 5.8 I = thousands of automatic weapons y = murders per 100,000 residents Keep in mind, the data in the table do not necessarily imply a direct causal link between the two variables. (In fact, can you think of any reasons why these variables may not be directly related to each other?) Also, the purpose of this question is not to make a political statement about guns or gun control. It's only to illustrate that data such as these sometimes have mathematical relationships that can be modeled with linear equations. Those relationships should always be questioned and examined carefully. With that said, these data can be modeled by the equation y = 0.87z + 3.95. Use this equation to answer the following; A) If this model is oconsistent, how many murders per 100,000 residents can be expected in a state with 8.7…
What is the relative frequency of deaths that occurred in 2000 or before?
what is the predicted number of sleep hours for persons with 60 years of age?
Suppose the observational units in a study are the patients arriving at an emergency room on a given day, Nov 11, 2019. Number of patients that arrive before noon is a qualitative variable is a quantitative variable is not a variable
ABC company has recently experienced a sharp decline in its stock price. Its current (time 0) stock price is $42.65, and this morning, it paid $5.00 per share. Given that the stock price four years ago was $82.88, complete the following time series of data by determining the missing values. t= -3, Pt =$83.43, Divt = $16.03, Rt = (1)t= -2, Pt =$85.20, Divt =(2), Rt = 22.8482%t= -1, Pt =(3), Divt =$17.50, Rt = 20.6572%t= 0, Pt =$42.65, Divt =$5.00, Rt =(4)
Q1]. [30%] The data for this question given in the table below come from the sample data folder of MINITAB. The data give you the monthly number of people employed (in units of 1000) in the retail trade industry in the state of Wisconsin from January 1970 to December 1974. Copy this timeseries data into an excel spreadsheet in your home area first before you start your analysis. You can use Excel to answer this question. Month 1970 1971 1972 1973 1974 1 322 322 330 348 361 2 317 318 326 345 354 3 319 320 329 349 357 4 323 326 337 355 367 5 327 332 345 362 376 6 328 334 350 367 381 7 325 335 351 366 381 8 326 336 354 370 383 9 330 335 355 371 384 10 334 338 357 375 387 11 337 342 362 380 392 12 341 348 368 385 396 (a) (b) (c) (d) Draw a time series plot of the data and comment on any noticeable patterns. Describe an appropriate time series model for this data, giving your reasons, and obtain estimates of the components of your model using the decomposition method. Using your model…
A psychology student conducted a study on using a chief executive officer's facial structure to predict a firm's financial performance. The facial width-to-height ratio (WHR) for each in a sample of 55 CEOs at publicly traded firms was determined. The sample resulted in x=1.74 and s=2.02. The student wants to predict the financial performance of a firm based on the value of the true mean facial WHR of CEOs. The student wants to use the value of μ=2.3. Do you recommend he use this value? Conduct a test of hypothesis for μ to help you answer the question. Specify all the elements of the test, including H0, Ha, test statistic, p-value, and your conclusion. Test at α=0.10. Find the p-value. p-value=enter your response here (
An observational study of teams fishing for the red spiny lobster in a certain body of water was conducted and the results published in a science magazine. One of the variables of interest was the average distance separating traps - called "trap spacing" - deployed by the same team of fishermen. Trap spacing measurements (in meters) for a sample of seven teams of fishermen are shown in the accompanying table. Of interest is the mean trap spacing for the population of red spiny lobster fishermen fishing in this body of water. Complete parts a through f below. E Click the icon to view the trap spacing data. Trap Spacing Data a. Identify the target parameter for this study. The target parameter for this study is 93 101 107 95 81 68 84 b. Compute a point estimate of the target parameter. (Round to two decimal places as needed.) Print Done c. What is the problem with using the normal (z) statistic to finda confidence interval for the target parameter? O A. The point estimate is too large to…
The data used is from college campuses. The variables used in the analysis below include: crime, total campus crime; enroll, total enrollment; police, employed officers. Use the estimated OLS models to answer the questions below: Model A: In(crime) = -6.631 + 1.270ln(enroll), (1.034) (.110) .5804 n = 97; R² = Model B: In(crime) = -4.794+.923ln(enroll) +.516ln(police), (.144) (.149) (1.112) n = 97; R² = .632 Using Model A, test the null hypothesis that elasticity of crime with respect to enrollment is unit elastic, i.e. equal to one (against a two-sided alternative). What is the conclusion of your test using a significance level of .01? Reject Fail to reject O Not enough information.
The amount of money earned in a day by Ms. Anne’s craft shop was recorded and displayed in the stem-and-leaf diagram shown below in Exhibit 1. Exhibit 1 Stem-and-leaf of Data: Amount of Money earned in a day in dollars Leaf Unit = 10 N = * 2 2595 323413 3 55779999 (**) 4 0001233 14 4 5569 *** 5 033445 5 7788 161 What is the median of the distribution? Use the stem-and-leaf diagram above to draw a box-and-whisker diagram. Comment on the skewness of the distribution.

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