1. The following data represent the number of flash drives sold per day at a local computer shop and their prices. | Price (x)| Units Sold (y)|
| $34| 3|
| 36| 4|
| 32| 6|
| 35| 5|
| 30| 9|
| 38| 2|
| 40| 1|

| a. Develop as scatter diagram for these data. b. What does the scatter diagram indicate about the relationship between the two variables? c. Develop the estimated regression equation and explain what the slope of the line indicates. d. Compute the coefficient of determination and comment on the strength of relationship between x and y. e. Compute the sample correlation coefficient between the price and the number of flash drives sold. f. Perform a t test and determine if the price and the number of flash drives sold are related. Let α = 0.01. g. Perform an F test and determine if the price and the number of flash drives sold are related. Let α = 0.01.|

ANS:
b. Negative linear relationship.
c.| = 29.7857 - 0.7286xThe slope indicates that as the price goes up by $1, the number of units sold goes down by 0.7286 units.| d.| r 2 = .8556; 85.56% of the variability in y is explained by the linear relationship between x and y.| e.| rxy = -0.92; negative strong relationship.|

f. t = -5.44 < -4.032 (df = 5); reject Ho, and conclude x and y are related. g.| F = 29.642 > 16.26; reject Ho, x and y are related.|

2. Assume you have noted the following prices for books and the number of pages that each book contains.

| a. Develop as scatter diagram for these data. b. What does the scatter diagram indicate about the relationship between the two variables? c. Develop the estimated regression equation and explain what...

...Chapter 4 Simpleregressionmodel Practice problems
Use Chapter 4 Powerpoint question 4.1 to answer the following questions:
1. Report the Eveiw output for regressionmodel .
Please write down your fitted regressionmodel.
2. Are the sign for consistent with your expectation, explain?
3. Hypothesize the sign of the coefficient and test your hypothesis at 5% significance level using t-table.
4. What percentage of variation in 30 year fixed mortgage rate is explained by this model? Why?
Use Chapter 4 Powerpoint question 4.2 to answer the following questions:
5. Report the Eveiw output for regressionmodel
Based on the estimation period of 1986.01 – 1999.07. Please write down your fitted regressionmodel.
6. Is Trend correlated with USPI? Set up the hypothesis testing at 5% significance level.
7. What percentage of variation in USPI is explained by this model? Why?
8. Based on your Eview model, report your forecast of USPI for the period of 1999.08-2000.07. Report RMSE.
Use Chapter 4 Powerpoint question 4.3 to answer the following questions:
9. Report the Eveiw output for regressionmodel USPIt = (USTBR)t + t based on the estimation period of 1986.01 – 1999.07. Please write down your...

...
• All numerical calculations and graphs/plots should be done using EXCEL.
• A hard copy of your completed assignment must be submitted electronically with the Griffith OUA Cover Sheet (available in the Assessment section of the unit website) attached as the 1st page of your submission. See instruction on the IBA134 Business Statistics unit website under “Assessment” and “Online submission of assignments using SafeAssign” on the link https://learning.secure.griffith.edu.au/webapps/portal/frameset.jsp?tab=courses&url=/bin/common/course.pl?course_id=_111213_1&frame=top
• You assignment must be in a Word doc format – no pdfs!
• When answering questions, wherever required, you should cut and paste the Excel output (eg, plots, regression output etc) to show your working on your assignment.
• You are required to keep a hard copy and an electronic copy of your submitted assignment to re-submit, in case the original submission is lost for some reason.
Important Notice:
As this is an individual assessment item, students should work on their own and present their individual assignment submission. If found to have cheated, all submissions involved would receive a mark of zero for this assessment item.
Discussions related to the assignment will not be allowed on the Discussion Board.
Computer Assignment Problem
Answer all SIX Questions
Some critics of television complain that the amount of violence shown on...

...TRUE
2. In a simpleregression analysis the error terms are assumed to be independent and normally distributed with zero mean and constant variance. – TRUE
3. The difference between the actual Y-value and the predicted Y-value found using a regression equation is called the residual (ε) – TRUE
4. In a multiple regression analysis with N observations and k independent variables, the degrees of freedom for the residual error is given by (N-k). – FALSE (correct answer N-k-1)
5. From the following scatter plot, we can say that between y & x there is _______. – Negative correlation
6. According to the graph, X & Y have ________. – Virtually no correlation
7. A cost accountant is developing a regressionmodel to predict the total cost of producing a batch of printed circuit boards as a function of batch size (the number of boards produced in one lot or batch.) The explanatory variable is called the _______. – Coefficient of determination
8. In the regression equation, y = 75.65 + 0.50x, the intercept is ______. – 75.65
9. The assumptions underlying simpleregression analysis include ______. – The error terms are independent
10. The proportion of variability of the dependent variable accounted for or explained by the independent variable is called the _______. – coefficient of determination
11. A...

...EXECUTIVE SUMMARY
The study is undertaken to study retailers behavior towards Aircel in selected region. The data is collected directly by visiting outlets through structured interview scheduled. The statistical tools used to analyze the data are: Co-relation analysis, SimpleLinearRegression and Multiple LinearRegression. The software used to analyze the data is Windostat version 8.6, developed by Indostat services, is an advanced level statistical software for research and experimental data analysis.
The study is carried mainly in the areas like Lokthkunta, Lalbazar, Kharkhana, Old Alwal, Suraram, Medchal, Miyapur, Balanagar, Bollaram, Yapral, Anandbagh, Malkajgiri, ECIL areas in Hyderabad city.
1. INTRODUCTION
Telecommunication was one of the world powerful tool of development. It is one of the key changer for continuous growth and in areas of reducing poverty, employment development, gender equity, balanced regional development and special protection for vulnerable sections of the society. Indian telecommunication sector has undergone as a growth engine for the Indian economy in the last decade with the country experiencing huge growth in wireless sector. The penetration of internet and broadband has also improved.
Telecom sector is broadly divided into:
1. Fixed line telephony.
2. Mobile telephony.
a. Global System for Mobile Communications (GSM) and
b. Code Division...

...ASSOCIATIVE (CAUSAL) MODELS:-
There is a causal relationship between the variable to be forecast and another variable or a series of variables. (Demand is based on the policy, e.g. cement, and build material.
Causal Model:
Demand for next period
= f (number of permits, number of loan application....)
There is no logical link between the demand in the future and what has happened in the past. There are other factors which can be logically linked to the demand.
Example 1: There is a strong cause and effect relationship between future demand for doors and windows and the number of construction permits issued at present.
Example 2: The demand for new house or automobile is very much affected by the interest rates changed by banks.
Regression analysis is one such causal method. It is not limited to locating the straight line of best fit.
Types:-
1. Simple (or Bivariate) Regression Analysis:
Deals with a Single independent variable that determines the value of a dependent variable.
Ft+1 = f (x) t Where Ft+1: the forecast for the next period.
This indicates the future demand is a function of the value of the economic indicator at the
present time.
Demand Function: D=a+bP, where b is negative.
If we assume there is a linear relation between D and P, there may also be some random variation in this relation.
Sum of Squared Errors (SSE): This is a measure of the predictive...

...STATISTICS FOR MGT DECISIONS
FINAL EXAMINATION
Forecasting – SimpleLinearRegression Applications
Interpretation and Use of Computer Output (Results)
NAME
SECTION A – REGRESSION ANALYSIS AND FORECASTING
1) The management of an international hotel chain is in the process of evaluating the possible sites for a new unit on a beach resort. As part of the analysis, the management is interested in evaluating the relationship between the distance of a hotel from the beach and the hotel’s average occupancy rate for the season. A sample of 14 existing hotels in the area is chosen, and each hotel reports its average occupancy rate. The management records the hotel’s distance (in miles) from the beach. The following set of data is obtained:
Distance (miles) 0.1 0.1 0.2 0.3 0.4 0.4 0.5 0.6 0.7
Occupancy (%) 92 95 96 90 89 96 90 83 85
Continue
Distance (miles) 0.7 0.8 0.8 0.9 0.9
Occupancy (%) 80 78 76 72 75
Use the computer output to respond to the following questions:
a) A simplelinearregression was ran with the occupancy rate as the dependent (explained) variable and distance from the beach as the independent (explaining) variable
Occpnc = b[pic] + b[pic](Distncy)
What is the estimated regression equation?
The regressionmodel is: Occpnc = b[pic] + b[pic](Distncy)
The estimated...

...SIMPLE VERSUS MULTIPLE REGRESSION
The difference between simple and multiple regression is similar to the difference between one way and factorial ANOVA. Like one-way ANOVA, simpleregression analysis involves a single independent, or predictor variable and a single dependent, or outcome variable. This is the same number of variables used in a simple correlation analysis. The difference between a Pearson correlation coefficient and a simpleregression analysis is that whereas the correlation does not distinguish between independent and dependent variables, in a regression analysis there is always a designated predictor variable and a designated dependent variable. That is because the purpose of regression analysis is to make predictions about the value of the dependent variable given certain values of the predictor variable. This is a simple extension of a correlation analysis. If I am interested in the relationship between height and weight, for example, I could use simpleregression analysis to answer this question: If I know a man’s height, what would I predict his weight to be? Of course, the accuracy of my prediction will only be as good as my correlation will allow, with stronger correlations leading to more accurate predictions. Therefore, simple...