A1.Regression analysis is ____________________________________. A) describes the strength of this linear relationship.
B) describes the mathematical relationship between two variables. C) describes the pattern of the data.
D) describes the characteristic of independent variable.

A2.__________________ is used to illustrate any relationship between two variables. A) Histogram
B) Pie chart
C) Scatter diagram
D) Frequency polygon

Questions A3 to A5 relate to the following information.

Suppose a firm fed the values of turnover, y, and advertising expenditure, x, (both in $000) for the past eight years, into a computer and obtained the regression relationship y = 26.7 + 8.5x.

A3.What is the dependent variable?
A) Number of computers
B) Size of the firm
C) Turnover
D) Advertising expenditure

A4.What is the independent variable?
A) Number of computers
B) Size of the firm
C) Turnover
D) Advertising expenditure

A5.If the advertising expenditure is $5000 in a particular year, estimate the turnover for that year. A) $69,200
B) $42,526.70
C) $26.7
D) $69.20

A6.Explain what the following sample correlation coefficients tell you about the relationship between the x and y values in the sample: r = - 0.8
A) No correlation.
B) Perfect negative correlation.
C) Strong negative correlation.
D) Weak negative correlation.

A7.What is meant by time-series data?
(A)A set of values which occurs sequentially in time.
(B)A set of qualitative data.
(C)A set of values which occurs randomly.
(D)A set of marks obtained by a group of students.

A8.The classical approach to time series analysis identifies four influences or components on the time series. Which of the following is NOT a time-series component?...

....2.3 Timeseries models
Timeseries is an ordered sequence of values of a variable at equally spaced time intervals. Timeseries occur frequently when looking at industrial data. The essential difference between modeling data via timeseries methods and the other methods is that Timeseriesanalysis accounts for the fact that data points taken over time may have an internal structure such as autocorrelation, trend or seasonal variation that should be accounted for. A Time-series model explains a variable with regard to its own past and a random disturbance term. Special attention is paid to exploring the historic trends and patterns (such as seasonality) of the timeseries involved, and to predict the future of this series based on the trends and patterns identified in the model. Since timeseries models only require historical observations of a variable, it is less costly in data collection and model estimation.
. Timeseries models can broadly be categorized into linear and nonlinear Models. Linea models depend linearly on previous data points. They include the autoregressive (AR) models, the integrated (I) models, and the moving average (MA)...

...
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...

...TimeSeriesAnalysis
This (not surprisingly) concerns the analysis of data collected over time ... weekly values, monthly values, quarterly values, yearly values, etc. Usually the intent is to discern whether there is some pattern in the values collected to date, with the intention of short term forecasting (to use as the basis of business decisions). We will write yt = response of interest at time t (we usually think of these as equally spaced in clock time). Standard analyses of business timeseries involve:
1) smoothing/trend assessment
2) assessment of/accounting for seasonality
3) assessment of/exploiting "serial correlation"
These are usually/most eﬀectively done on a scale where the "local" variation in yt is approximately constant.
Smoothing TimeSeries
There are various fairly simple smoothing/averaging methods. Two are "ordinary moving averages" and "exponentially weighted moving averages."
Ordinary Moving Averages For a "span" of k periods,
e yt = moving average through time t yt + yt−1 + yt−2 + · · · + yt−k−1 = k Where seasonal eﬀects are expected, it is standard to use
k = number of periods per cycle
Exponentially Weighted Moving Averages These weight observations less heavily as one moves back in time from the current period. They are typically...

...QUESTION 21
The finishing process on new furniture leaves slight blemishes. The table below displays a manager's probability assessment of the number of blemishes on one piece of new furniture.
Number of Blemishes
0
1
2
3
4
5
Probability
0.34
0.25
0.19
0.11
0.07
0.04
1. On average, how many blemishes do we expect on one piece of new furniture?
2. What is the variance of blemishes on one piece of new furniture? (round to the nearest hundredth) QUESTION 22
The probability that a person catches a cold during the cold-and-flu season is 0.4. Assume that 10 people are chosen at random.
On average, how many of these ten people would you expect to catch a cold?
What is the standard deviation of the number of people who catch a cold? (round to the nearest hundredth)
QUESTION 23
The number of nails in a five-pound box is normally distributed with a mean of 566 and a standard deviation of 33.
What is the probability that there are less than 500 nails in a randomly-selected five-pound box of nails? (express as a decimal, not a percentage)
The probability is 0.99 that a randomly-selected five-pound box of nails contains at least how many nails approximately?
QUESTION 24
You are the owner of a small casino in Las Vegas and you would like to reward the high-rollers who come to your casino. In particular, you want to give free accommodations to no more than 10% of your patrons....

...2011
Lecturer: Thilo Klein
Contact: tk375@cam.ac.uk
Contest Quiz 6
Question Sheet
In this quiz we will review non-linearity and model transformations covered in lectures 6 and 7.
Question 1: Logarithms
(i) The interpretation of the slope coefficient in the model 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 1% change in X is associated with a change in Y of 0.01 β1 .
(c) a change in X by one unit is associated with a β1 100% change in Y.
(d) a change in X by one unit is associated with a β1 change in Y.
(ii) The interpretation of the slope coefficient in the model ln(Yi ) = β0 + β1 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 100β1 % change in Y.
(c) a 1% change in X is associated with a change in Y of 0.01β1 .
(d) a change in X by one unit is associated with a β1 change in Y.
(iii) The interpretation of the slope coefficient in 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)...

...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...

...RegressionAnalysis (Tom’s Used Mustangs)
Irving Campus
GM 533: Applied Managerial Statistics
04/19/2012
Memo
To:
From:
Date: April 19st, 2012
Re: StatisticAnalysis 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...

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