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Regression Analysis

Regression Analysis of Computer Software

A computer software developer would like to use the number of downloads (in thousands) for the trial version of his new shareware to predict the amount of revenue (in thousands of dollars) he can make on the full version of the new shareware. Following is the not complete output from a simple linear regression along with the re

Correlation Matrix and Multiple Regression Output

I am having a difficult time with this question. Variables Defined: Lottery: How many times a sampled customer has purchased California lottery tickets in the past two months Education: How many years of education the sampled customer has completed Age: The age of the sampled customer in years Children: How many chi

Multiple regression: City MPG

Perform a complete multiple regression using City MPG as the response variables. Assess the model using the steps as performed/outlined (correlation matrix, f-test, t-tests, r-sq and standard error). If the full multiple regression needs modification, perform a stepwise regression and select the final model. Briefly note why you

Regression of MedIncome

a. Perform a simple regression using MedIncome as the explanatory variable for Sale/Sq Ft. Assess the model by performing the test of significance on the slope, determining and interpreting R-sq and briefly assessing the standard error of the estimate. b. Perform a simple regression using MedAge as the explanatory variable f

Linear Regression Relationship Analysis

Ten students in a graduate program were randomly selected. Their grade point averages (GPAs) when they entered the program were between 3.5 and 4.0. The following data were obtained regarding their GPAs on entering the program versus their current GPAs: Entering GPA 3.5 3.8 3.9 3.7 4 4 3.6 3.9 3.7

Correlation Revelation/chi-squared analysis

Regression and Correlation are two of the most often used and abused tools in research. People are quick to jump to conclusions that if a relationship exists between two variables, then one must cause (causation) the other. There are many reasons why two variables can be related without causality. Please respond to the following

Understanding Regression Analysis

25. A real estate analyst uses regression analysis to determine the relationship between interest rates and house sales. She collects the following data and obtains the results below. Interest Rates % (X) Number of Houses Sold (Y) 7 64 8 52 9 42 10 34 11 32 12 31 13 30 14 29 15 19 Y-hat = 87.6 - 4.6X, where X

AirPassengers time series analysis with R

The data file contains the monthly total international airline passengers bookings (in thousands) from January 1949 to December 1960. The data are available as a time series in R under the variable AP. Do an exhaustive analysis of the data and give the R code used for each step: Describe the trend, seasonality, and irregular c

Mean Absolute Percent Error

An electronic test equipment manufacturing company markets a certain piece of specialty equipment. The company has several competitors who currently market similar pieces of equipment. hile customers have repeatedly indicated they prefer the company's equipment, they have historically proven to be unwilling to wait for the comp

Statistical Analysis: Forecasting Problem

Please see the attached case study. 1. Develop a forecasting model, justifying its selection over other techniques, and project attendance through 2007. 2. What revenues are to be expected in 2006 and 2007? 3. Discuss the school's options.

Regression Analysis: Air Pollution

The Questions, and the data needed is in the attachment. 1. Select 2 variables X and Y in your data set for a regression analysis. Display the scatter plot of the data. 2. Fit the simple linear regression model Y=ß0 +ß1 X + e to the data (X1, X2)..... (Xn, Yn). Report the least square estimates ß^0 and ß^1. You can use

Multiple Regression Analysis - Experience Levels

Refer to the data on fixing breakdowns in cell phone relay towers in the table on TAB 2. In the initial design, experience level was coded as Novice or Guru. Now consider three levels of experience: Novice, Guru and Experienced. Some additional runs for an experienced engineer are given below. Also, in the original data set, rec

Finding The Regression Equation Using Exam Scores

In an introductory stats class, x = midterm exam score and y = final exam score. The mean midterm score is 75 with a standard deviation of 12. The mean final exam score is 70 with a standard deviation of 10. The correlation between the exam scores is 0.68. How does one find the regression equation?

Regression Model Development and Analysis

Regression model development and assess the future employment scenario in developing countries (Please refer to attachment 'Table 1') Answers may address the following items: 1) Comment on the relevance of data presented in Table 1 (attached Excel file) and briefly describe the approach you would like to adopt in developin

Obesity, adiposity and biostatistical calculations

Obesity is very common in American society and is a risk factor for breast cancer in postmenopausal women. One mechanism explaining why obesity is a risk factor is that it may raise estrogen levels in women. In particular, one type of estrogen, serum estradiol, is a strong risk factor for breast cancer. To better assess these re

Regression Analysis: Random Selection

The expense ratio of a mutual fund measures the percentage of the fund's assets used to pay for annual administrative overhead. Funds with higher ratios spend more of the fund's return to operate the fund. A random selection of 16 stock mutual funds last year revealed the following data: Expense Ratio, X 0.62 1.21 1.03 0

Correlation Types and Statistical Analysis

See attached case study. 1) What descriptive statistics were reported in the attached case study? 2) What statistical tests were conducted to analyze the data? 3) Were there any significant findings reported in the attached case study? If so, provide a description or interpretation of the results. 4) What relation

Trend Projection

Please help me with the attached question. Using a trend projection, forecast the demand for Period 9

Significance of the regression

Please help me with the attached question. Thank you. Use the following data on mother's shoe size and infant birth weight a) construct a scatter plot for these data b) using alpha = 0.10, test for the significance of the regression slope c) construct a 90% confidence interval for the population slope. d) discuss your

Regression Analysis - Independent and Dependent Variables

1. Conduct a regression analysis to determine if age (independent variable) has any effect on the number of vitamins/supplements people take. State what you are testing and what the x (independent) and y (dependent) variables are. Test for correlation and explain the regression results, if necessary. 2. Conduct a regression a

Regression Model of Independent Variables

Consider the set of dependent and independent variables that can be found in the attached Excel file Prob 151.xlsx. a) construct a regression model using both independent variables b) test the significance of each independent variable using alpha = 0.05 c) interpret the p-value for each independent variable Data set y x

quantitative forecasting

"... With quantitative forecasting, historical data and math are used to predict future events" . Questions: A) Identify a variable that varies over time. Be sure you have a sufficiently large data set. B) Construct a time series scatter plot. Is there an apparent trend component? (plug data into Excel, create simple scatt

Regression, Outliers, P-value, R

Use the dataset anscomb.sav and SPSS to answer the following questions: Problem 9.1: a. Produce the scatter plot for x3 and y3. Do you think there are any outliers? b. Calculate the correlation coefficient (r) for all the data points c. What does the p-value in your SPSS output tell you? d. What does r tell you about the

Researching Using the Quantitative Method

I need to come up with a social science problem that has been studied using the quantitative research method. In doing so I could also use help finding a peer-reviewed source to go along with it.

Time Series Analysis: Independent Stationary Processes

Two processes {Z(t)} and {Y(t)} are said to be independent if for any time points t(1), t(2), ... t(m) and s(1), s(2),...s(n) the random variables {Z(t), Z(t2),...Z(tm)} are independent of the random variables {Y(s1), Y(s2),...Y(sn)}. Show that if {Z(t)} and {Y(t)} are independent stationary processes, then W(t) = Z(t) + Y(t) is

Weighted Moving Average Using Multiple Regression

1. Use Data Analysis from the attached excel file to create a multiple regression equation where the forecast for the next year is a function of the attendance in each of the previous 2 years. That is: F(t)=a + b1*D(t-1)+b2*D(t-2) Where: F is the forecast for period t a is the intercept bj is the regression coefficient

Application of Regression Analysis

1. Use the graph (see attachment) to describe the total variation about a regression line in words and in symbols. 3. Use the graph (see attachment) to describe the unexplained variation about a regression line in words and in symbols. 11. Retail Space and Sales - The following table (see attachment) represents the total s