### Seafood restaurant

The manager of a seafood restaurant was asked to establish a pricing policy on lobster dinners. Experimenting with prices produced the following data: Average Number Sold per Day, y Price, x 200 $6.00 190

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The manager of a seafood restaurant was asked to establish a pricing policy on lobster dinners. Experimenting with prices produced the following data: Average Number Sold per Day, y Price, x 200 $6.00 190

Use the data in the table, which shows the personal income and outlays (both in trillions of dollars) for Americans for 11 recent years. (Source: U.S. Commerce Department, Bureau of Economic Analysis) Personal income, x Personal outlays, y 5.6 4.6 5.8 4.9 6.2 5.2 6.5 5.5 6.9 5.8 7.4 6.1 7.8

The following data gives the starting salary for students who recently graduated from a local university and accepted jobs soon after graduation. The starting salary, grade jobs soon after graduation. The starting salary, grade- point average (GPA), and major (business or other) are provided. Salary $29,500 $46,000 $39,800 $36,

Calculate the regression equation using Excel, including the R Square, Slope, Intercept and the formula Y = a +bX with % Welfare as the X variable and % Passing as the Y variable.

I. Simple Regression to Estimate Parameters. (A) Simple regression model with sales revenue as the dependent Y-variable and R&D expenditures independent X-variable. (B) Simple regression model with profits as the dependent Y-variable and R&D expenditures independent X-variable.

Prepare a report using Excel as your processing tool to process three simple regression analyses. 1. First run a regression analysis using the BENEFITS column of all data points in the data set as the independent variable and the INTRINSIC job satisfaction column of all data points in the data set as the dependent variable.

Residual Plot: Consider the data in the table below. A. Examine the data and identify the relationship between x & y. B. Find the linear correlation coefficient & use it to determine whether there appears to be a significant linear correlation between x & y. C. Construct a scatter plot. What does it suggest about the relatio

Compare and contrast quantitative and qualitative research designs. Identify and discuss some advantages and disadvantages for each type of research.

Find the equation of the regression line for the given data. Use the regression equation to predict the value of y for each of the given x-values, if meaningful. The caloric content and the sodium content (in milligrams) for 6 beef hot dogs are shown below. Calories, X 160 170 130 130 90 180 Sodium, Y 415

Imagine you are a real estate investor presented with a regression analysis of home sales near one of your investment properties. Use Stat tools regression mod3l to determine: Which is a better predictor of selling price:appraised value, square footage, or number of bedrooms? A) How much value is added per $1000 OF APPR

Problem: Do Hispanics earn more than white individuals at a large company, for which lawsuit filed. Attached data include, 1. Employee ID, 2. Job title, 3. Ethnicity, 4. Yrs. Working. 1. Is pay different by ethnicity and if so are they statistically significant, and what is meaning of such. Consider some of the arguments t

Hi, I need help with this assignment. I am using a different Dataset then what is in the solution library. My data set is 0903A.Xls.My school uses Turn it which is a similarity score and if it matches too high The assignment mentions a DATA SET 903A which i have provided - I have attached this information. Thank you and ple

Bus Inc. sells widgets. Sales dept says there is a positive linear relationship between the advertising expenditures and sales. Sales department recently analyzed the sales over 42 weeks. For each week in the sample, Bus Inc sales (SALES) and their advertising expenditures (ADVERT) were recorded. A simple regression analysis was

An agent for a residential real estate company in a large city would like to be able to predict the monthly rental cost of apartments based on the size of the apartment.At the .05 level of significance determine if the correlation between rental cost and apartment size is significant? Rent Size 950 850 1600 1450 1200 1085 1

Please execute an example where regression could be used to show a relationship between two variables.

The task is to provide evidence, for or against, common perceptions about property crime. Are crime rates higher in urban than rural areas? Does unemployment or education level contribute to property crime rates? How about public assistance? What other factors relate to property crimes? The file named Data File may help answer s

1. What is the variable used to predict the value of another called? A) Independent B) Dependent C) Correlation D) Determination 2. Based on the regression equation, we can A) predict the value of the dependent variable given a value of the independent variable. B) predict the value of the independent

Carpet City wants to develop a means to forecast its carpet sales. The store manager believes that the store's sales are directly related to the number of new housing starts in town. The manager has gathered data from county records on the monthly house construction permits and from store records on the monthly sales. a. Dev

Please see the attached 2 problems. Please provide a step by step solution and also use Excel to arrive at the answers. Thank you. Problem 1: The US Government has asked for your assistance in determining the acceptability of structural bolts provided by a supplier. The bolts must be 1.84 centimeters in diameter. You select

To understand a regression equation we need to understand what the variables are and what the parameters are. The variables in this case are "Nut Yield" in California, "precipitation" based on annual rainfall data, and "acres" which refers to the number of acres planted. Next are the parameters. "a" is the intercept. It te

For my project I plan to pull information from the State of California, Department of Agriculture on the nut crop yields. They have historical information on prior years yields. I also plan to pull a couple of other variables that impact yields like rainfall and acres planted. Using this historical information I want to be able

The owner of Maumee Ford-Mercury wants to study the relationship between the age of a car and its selling price. Listed below is a random sample of 12 used cars sold at Maumee Motors during the last year. Age (Years) SELLING PRICE ($000) 9 8.10 7

4. Market Planning, Inc. a marketing research firm, has obtained prescription sales data for 20 independent pharmacies (see below). In this table, the following variables are included: Sales over the past year (in units of $1,000), Floor space (square feet), Percentage of floor space dedicated to the pharmacy (square feet), Numb

Please take the data I provide and do the computations and graphing for me, then explain the results to me so that I can write a paper and give a presentation on it. Here is my assignment: Prepare a 1,000 - 1,500 word paper applying Regression and Correlation to a personal or business application the student is familiar

See attached file.

4-20 The following data give the selling price, square footage, number of bedrooms, and age of houses that have sold in a neighborhood in the last 6 months. Develop three regression models to predict the selling price based upon each of the other factors individually. Which of these is best?

I need help answering these questions. Enclosed in the multiple regression model that the questions refer too and also the Table A & B that are referred to in question 2 & 3. This case involves the decision to locate a new store at one of two candidate sites. The decision will be based on estimates of sales potential. Pro

1.Open the file pamsue.xls. First, move the column for sales so that it is the rightmost column (it is now to the right of comtype). If the old sales column remains but appears empty, delete that column. 2.Obtain a scatterplot of the sales on the vertical axis against comtype on the horizontal axis. This will give you a go

A human resources director, on learning about the regression effect, decides to hire people who have been fired by their previous employer for poor performance. He argues that the regression effect says people who perform very poorly in their previous job tend to perform well in their next job. Is this what the regression ef

Interpret the following: (a) Y = a + BX; Y = 3.5 + .7X, where Y = likelihood of buying a new car and X = total family income. (b) Y = a + BX; Y = 3.5 - .4X, where Y = likelihood of buying tickets to a rock concert and X = age.