1.6.1 Sources of data
Only secondary sources of data have been collected and used to determine the study. Data sources are previous ten-year’s annual report of the companies that we selected, Company information from Dhaka stocks exchange servers, and others official documents. 1.6.2Sampling Technique Used

1.6.2.1 Population: The population parameters involved in the study is the all textile and knitwear based companies in Bangladesh those are registered as the company act 1991,BD

1.6.2.2 Sampling Design and Sample Size: The credit cardholders both in PBL and BRAC Bank Ltd. are addressed for the interview. Because the population size of credit cardholders is large, it is difficult to determine a representative sample size. So, a judgment sampling method is used for the cardholders because here the researcher’s judgment is used for selecting items which she considers as representative of the population. The total sample size of credit cardholders that has interviewed is 50 (25 of PBL, 25 of BBL). The Cardholders are selected on a convenient basis for the face-to-face personal interview as well as the telephone interview.

1.6.3Data Administering Method
Data was collected using convenient sampling technique. A structured self administered questionnaire for PBL credit cardholders and BBL credit cardholders were prepared. The cardholders and the most knowledgeable persons of the card division of the issuers were personally interviewed. Due to some inconvenience and also to avoid wastage of time, some respondents were also interviewed over the telephone by making them understand the technical terms.

1.6.4 Techniques of Data Analysis
Both qualitative and quantitative statistical techniques were used for this research. Graphical presentation tools like pie charts and bar graphs were used to present the collected data. For quantitative analysis, Mean score of relative importance (MSRI) was used to assess importance of factors by...

...
Unit 5 – RegressionAnalysis
Mikeja R. Cherry
American InterContinental University
Abstract
In this brief, I will demonstrate selected perceptions of the company Nordstrom, Inc., a retailer that specializes in fashion apparel with over 12 million dollars in sales last year. I will research, review, and analyze perceptions of the company, create graphs to show qualitative and quantitative analysis, and provide a summary of my findings.
Introduction
Nordstrom, Inc. is a retailer that specializes in fashion apparel for men, women and kids that was founded in 1901. The company is headquartered in Seattle, Washington with over 61,000 employees world-wide as of February 2, 2013. (Business Wire, 2014)
Nordstrom, Inc. offers on online store, e-commerce, retail stores, mobile commerce and catalogs to its consumers. It operates 117 full-line stores within the United States and 1 store in Canada, 167 Nordstrom Rack stores, 1 clearance store under the Last Chance Banner, 1 philanthropic treasure & bond store called Trunk Club and 2 Jeffrey boutiques. The option of shopping online is also available at www.nordstrom.com along with an online private sale subsidiary Hautelook. They have warehouses, also called fulfillment centers, which manages majority of their shipping needs that are located in Cedar Rapids, Iowa. (Business Source Premier, 2014)
Nordstrom, Inc. continues to make investments in their e-commerce...

...the model ln(Yi ) = β0 + β1 ln(Xi ) + ui is as
follows:
(a) a 1% change in X is associated with a β1 % change in Y.
(b) a change in X by one unit is associated with a β1 change in Y.
(c) a change in X by one unit is associated with a 100β1 % change in Y.
(d) a 1% change in X is associated with a change in Y of 0.01β1 .
(iv) To decide whether Yi = β0 + β1 X + ui or ln(Yi ) = β0 + β1 X + ui fits the data better, you
cannot consult the regression R2 because
(a) ln(Y) may be negative for 0 < Y < 1.
(b) the TSS are not measured in the same units between the two models.
(c) the slope no longer indicates the effect of a unit change of X on Y in the log-linear
model.
(d) the regression R2 can be greater than one in the second model.
1
(v) The exponential function
(a) is the inverse of the natural logarithm function.
(b) does not play an important role in modeling nonlinear regression functions in econometrics.
(c) can be written as exp(ex ).
(d) is ex , where e is 3.1415...
(vi) The following are properties of the logarithm function with the exception of
(a) ln(1/x) = −ln(x).
(b) ln(a + x) = ln(a) + ln(x).
(c) ln(ax) = ln(a) + ln(x).
(d) ln(xa) = aln(x).
(vii) In the log-log model, the slope coefficient indicates
(a) the effect that a unit change in X has on Y.
(b) the elasticity of Y with respect to X.
(c) ∆Y/∆X.
(d)
∆Y
∆X
×
Y
X
(viii) In the model ln(Yi ) = β0 + β1 Xi + ui , the elasticity of E(Y|X) with respect to X...

...Introduction
This presentation on RegressionAnalysis will relate to a simple regression model. Initially, the regression model and the regression equation will be explored. As well, there will be a brief look into estimated regression equation. This case study that will be used involves a large Chinese Food restaurant chain.
Business Case
In this instance, the restaurant chain's management wants to determine the best locations in which to expand their restaurant business. So far the most successful locations have been near college campuses. This opinion is based on the positive numbers that quarterly sales (y) reflect and the size of the student population (x). Management's mindset is that over all, the restaurants that are within close proximity to college campuses with large student bodies generate more sales than restaurants located near campuses with small student bodies.
In the sample box below, xi is the size of the student population (in thousands) and yi is the quarterly sale (in thousands of dollars). The value for xi and yi for all of the 10 Chinese Food restaurants given in the sample are reflected as follows:
Sample Data:
(measured in 1,000s) (measured in $1,000s)
Restaurant Student Population Quarterly Sales
(i) (xi) (yi)
1 2 58
2 6 105
3 8 88
4 8 118
5 12 117
6 16 137
7 20 157
8 20 169
9 22 149
10 26 202
Methodology
Given the...

...RegressionAnalysis Exercises
1- A farmer wanted to find the relationship between the amount of fertilizer used and the yield of corn. He selected seven acres of his land on which he used different amounts of fertilizer to grow corn. The following table gives the amount (in pounds) of fertilizer used and the yield (in bushels) of corn for each of the seven acres.
|Fertilizer Used |Yield of Corn |
|120 |138 |
|80 |112 |
|100 |129 |
|70 |96 |
|88 |119 |
|75 |104 |
|110 |134 |
a. With the amount of fertilizer used as an independent variable and yield of corn as a...

...Assignment # 1
Forecasting (Total marks: 100)
Following 10 Problems are for submission
Problem 1: [12]
Registration numbers for an accounting seminar over the past 10 weeks are shown below:
|Week 1 2 3 4 5 6 7 8 9 10 |
|Registrations 24 23 28 30 38 32 36 40 44 40 |
a) Starting with week 2 and ending with week 11, forecast registrations using the naive forecasting method. [2]
b) Starting with week 3 and ending with week 11, forecast registration using a two-week moving average. [3]
c) Starting with week 5 and ending with week 11, forecast registrations using a four-week moving average. [3]
d) Plot the original data and the three forecasts on the same graph. Which forecast smoothes the data the most? Which forecast responds to change the best? [4]
Problem 2 [4]
Given the following data, use exponential smoothing (( = 0.3) to develop a demand forecast. Assume the forecast for the initial period is 5.
|Period 1 2 3 4 5 6 |
|Demand 7 9 5 9 13 8 |
Problem 3 [6]
Calculate (a) MAD and (b) MSE for the following...

...
Mortality Rates
RegressionAnalysis of Multiple Variables
Neil Bhatt
993569302
Sta 108 P. Burman
11 total pages
The question being posed in this experiment is to understand whether or not pollution has an impact on the mortality rate. Taking data from 60 cities (n=60) where the responsive variable Y = mortality rate per population of 100,000, whose variables include Education, Percent of the population that is nonwhite, percent of population that is deemed poor, the precipitation, the amount sulfur dioxide, and amount of nitrogen dioxide.
Data:
60 Standard Metropolitan Statistical Area (SMSA) in the United States, obtained for the years 1959-1961. [Source: GC McDonald and JS Ayers, “Some applications of the ‘Chernoff Faces’: a technique for graphically representing multivariate data”, in Graphical Representation of Multivariate Data, Academic Press, 1978.
Taking the data, we can construct a matrix plot of the data in order to take a visible look at whether a correlation seems to exist or not prior to calculations.
Data Distribution:
Scatter Plot Matrix
As one can observe there seems to be a cluster of data situated on what appears to be a correlation of relationship between Y=Mortality rate and X= potential variables influencing Y.
From this we construct a correlation matrix in order to see a relationship in matrix form....

...REGRESSIONANALYSIS
Correlation only indicates the degree and direction of relationship between two variables. It does not, necessarily connote a cause-effect relationship. Even when there are grounds to believe the causal relationship exits, correlation does not tell us which variable is the cause and which, the effect. For example, the demand for a commodity and its price will generally be found to be correlated, but the question whether demand depends on price or vice-versa; will not be answered by correlation.
The dictionary meaning of the ‘regression’ is the act of the returning or going back. The term ‘regression’ was first used by Francis Galton in 1877 while studying the relationship between the heights of fathers and sons.
“Regression is the measure of the average relationship between two or more variables in terms of the original units of data.”
The line of regression is the line, which gives the best estimate to the values of one variable for any specific values of other variables.
For two variables on regressionanalysis, there are two regression lines. One line as the regression of x on y and other is for regression of y on x.
These two regression line show the average relationship between the two variables. The regression line of y on x gives the most probable...

...RegressionAnalysis (Tom’s Used Mustangs)
Irving Campus
GM 533: Applied Managerial Statistics
04/19/2012
Memo
To:
From:
Date: April 19st, 2012
Re: Statistic Analysis on price settings
Various hypothesis tests were compared as well as several multiple regressions in order to identify the factors that would manipulate the selling price of Ford Mustangs. The data being used contains observations on 35 used Mustangs and 10 different characteristics.
The test hypothesis that price is dependent on whether the car is convertible is superior to the other hypothesis tests conducted. The analysis performed showed that the test hypothesis with the smallest P-value was favorable, convertible cars had the smallest P-value.
The data that is used in this regressionanalysis to find the proper equation model for the relationship between price, age and mileage is from the Bryant/Smith Case 7 Tom’s Used Mustangs. As described in the case, the used car sales are determined largely by Tom’s gut feeling to determine his asking prices.
The most effective hypothesis test that exhibits a relationship with the mean price is if the car is convertible. The RegressionAnalysis is conducted to see if there is any relationship between the price and mileage, color, owner and age and GT. After running several models with different independent...