Scheduled maintenance: Thursday, December 8 from 5PM to 6PM PST
hello quizlet
Home
Subjects
Expert solutions
Create
Study sets, textbooks, questions
Log in
Sign up
Upgrade to remove ads
Only $35.99/year
Marketing Research Final
Flashcards
Learn
Test
Match
Flashcards
Learn
Test
Match
Terms in this set (69)
Chapter 13
...
Raw Data
Numbers and words with little meaning
Basic Statistical Tools for Summarizing Info From Data:
-Frequency distributions
-Proportions
-Measures of central tendency and dispersion
Inferential Statistics
Allow inferences about a whole population from a sample; primary purpose is to make a judgement about the population
Sample
Subset or small fraction of the total number of elements in the population
Sample Statistics
Measures computed from sample data
Population Parameters
Measured characteristics of a specific population
Application of Statistics:
-To describe characteristics of the population or sample
-To generalize from a sample to a population
Frequency Distributions
Constructing a frequency table (frequency distribution) is one of the most common means of summarizing a set of data
Proportions
Indicates the percentage of population elements that successfully meet some standard on the particular characteristic
Top Box Score
Refers to the portion of respondents who choose the most favorable response toward a company; portion that would highly recommend a business to others or the portion expressing the highest likelihood of doing business again
Bottom Box Score
Portion of respondents who choose the least favorable response to some question about customer opinion; more diagnostic of customer problems; signals need for some managerial reaction
Mean
Arithmetic average; common measure of central tendency; sum of all observations divided by number of observations
-Sample mean (x bar)- can be calculated when there is not enough data to calculate the population mean (u)
Median
Midpoint of the distribution; the 50th percentile; better measure of central tendency in the presence of extreme values or outliers
Mode
Measure of central tendency that merely identifies the value that occurs most often; used for data that is less than interval, with one large peak
Range
Simplest measure of dispersion; distance between the smallest and largest values of a frequency distributions
-Does not take into account all the observations
-Indicates extreme values of the distribution
Deviation Scores
Method of calculating how far any observations is from the mean to calculate individual deviation scores
Why Use Standard Deviation?
Square root of the variance for distribution; most valuable index of dispersion
Normal Distribution (Normal Curve)
One of the most common probability distributions in statistics; bell shaped; almost all of its values are w/in +- 3 standard deviations from its mean
Standardized Normal Distribution
Specific normal curve with several characteristics
-Symmetrical about its mean
-Mean identifies its highest point (mode) and vertical line about which curve is symmetrical
-Infinite number of cases
-Has a mean of 0 and std deviation of 1
Computing Z Scores
Subtract the mean from the value to be transformed and divide it by the standard deviation
Population Distribution
Frequency distribution of the population elements
Sample Distribution
Illustrates the functional relationship between the possible values of some characteristic of 'n' cases drawn at random and the propbability associated with each value over all possible samples of size 'n'; frequency distribution of a sample; sample mean= x bar; sample standard deviation= S
Expected Value of the Statistic
Sampling distributions mean
-Expected value of the mean of the sample distribution= u
Standard Error of the Mean
S(x bar); standard deviation of the sampling distribution
Point Estimate
Estimate of the population mean in the form of a single value, usually the sample mean
Confidence Intervals
Estimate is based on the knowledge that [u= x bar +- small sampling error]
-Confidence level is a % or decimal that indicates the long-run profitability that the results will be correct
Calculating CI
-Calculate x bar from the sample
-(Assuming u is known), estimate the population standard deviation by finding S (sample deviation)
-Estimate std error of the mean
-Determine Z-value associate for confidence level desired, then divide by 2
-Calculate CI
Random Sampling Error
Varies with samples of different sizes
Increasing Sample Size
Decreases the width of the CI at a given confidence level
Factors Required to Specify Sample Size:
-Variance, or heterogeneity of the pop
-Magnitude of acceptable error
-Confidence level
Variance
Refers to the std deviation of the population parameter; increases as sample size increases
Magnitude of Error (E)
Indicates a certain percision level
Chapter 14
...
Descriptive Analysis
Elementary transformation of data to describe basic characteristics such as central tendency, distribution, and variability
-Averages, medians, modes, range and standard deviations
-Can summarize responses from large #s
-Used to make inferences about characteristics of the entire pop of interest
Nature of Descriptive Analysis:
-Uses univariate (one variable) statistics
-Simple but powerful and widely used
Histogram
Graphical way of showing a frequency distribution in which the height of a bar corresponds to the frequency of a category
Tabulation
Orderly arrangement of data in a table or other summary format; tells the researcher how frequently each response occurs
Frequency Table
Yielded from counting the different ways respondents answer a question and arranging them in a simple tabular form
Variable's Frequency Distribution
Actual number of responses to each category
Marginal Tabulation
Simple tabulation; tells how frequently each response occurs
Frequency Column
Shows the tally result or the number of respondents for each category
Percent Column
Shows the total % in each category
Cumulative Percentage
Shows the % indicating either a particular category or any preceding category
Cross- Tabulation
An appropriate technique for addressing research questions involving relationships among multiple less-than interval variables
Contingency Tables
Data matrices that display the frequency of some combination of possible responses to multiple variables
-Two- way contingency tables most often used
-Beyond 3 variables, contingency tables become difficult
-Row and column totals are often called marginals
-Inner cells of most importance
Moderator Variable
3rd variable that changes the nature of a relationship between the original independent and dependent variables; specifies the conditions under which the relationship between the first two variables is strongest and weakest
Elaborate Analysis
Involves the basic cross-tabulation within various subgroups of the sample
Data Transformation
The process of changing data from their original form to a format suitable for performing a data analysis that will achieve the research objectives
Median Split
Collapse a scale with multiple response points into two categories; means respondents below the observed median go into one category and respondents above the median go in another
Hypothesis Testing Procedure
1) Hypothesis derived from the research objectives and should be stated as specifically as possible
2) Sample is obtained and the relevant variables are measured
3) Measured variable obtained in the sample is compared to the value either stated explicitly or implied in hypothesis
Significance Level
Becomes a key indicator of whether or not a hypothesis can be supported; critical probability associated with a statistical hypothesis test
P-Value
Probability value; the observed or computed significance level; low p-values mean there is little likelihood that the statistical exception is true
Type I Error
Occurs when a condition that is true in the population is rejected based on statistical observations; occurs when the researcher
concludes that there is a statistical difference
based on a sample result when
in reality one does not exist in the population
; generally considered more serious in marketing problems
Type II Error
Probability of failing to reject a false hypothesis; for correlation type relationships, the sample data suggests that a
relationship does not exist
when in fact a
relationship does exist
Beta
Name for the incorrect decision
Chapter 15
...
What Helps Determine the Analytical Approach?
1) How many independent and dependent variables are involved
2) What is the scale level of the independent and dependent variables involved
Cross-Tabulation
One of the most widely used and simplest techniques for describing sets of relationships; it is a joint frequency distribution of observations on two or more nominal or ordinal variables; compares observed frequencies with compared frequencies in each cell of the table
Goodness (closeness)- Of- Fit
Captured by cross tabulation statistics; allows us to conduct tests for significance in the analysis of the row x column contingency table
Testing the Hypothesis Involves 2 Steps:
1) Examine the statistical significance of the observed contingency table
2) Examine whether the differences between the observed and expected values are consistent with the hypothesized prediction
Independent Samples T-Test
T-test is appropriate when comparing means for a variable grouped into two categories; t-test is a function of the std error, which is a function of the std deviation; interpretation of the t-test is made by focusing on either the p-value or the confidence interval and the group means; t-tests can be used with large samples
Z-Tests
Should not be used with small samples; should be used in instances where the population variance is known ahead of time
Paired Samples T-Test
Appropriate when means that need to be compared are not from independent samples; when paired samples t-test is appropriate, the 2 numbers being compared are usually scored as separate variables; used when group of respondents have repeatedly been exposed to different tests and conditions under similar scales
One-Way Analysis of Variance (ANOVA)
Appropriate when the means of more than two groups or populations are to be compared; the appropriate statistical technique to examine the effect of a less than interval independent variable on an at least interval independent variable
Null Hypothesis
Test in which all the means are equal
F-Test
Key statistical test for an ANOVA model; determines whether there is more variability in the scores of one sample than in another sample; can be obtained by taking the larger sample variance and dividing by the smaller sample variance
General Linear Model
Ex= multivariate dependence techniques; way of modeling some process based on how different variables cause fluctuations from the average dependent variable
Multiple Regression Analysis
Allows one dependent variable to be explained by more than one independent variable
Students also viewed
Connect Assingments
55 terms
Final Exam Definitions
95 terms
IBA 555 Chapter 6
25 terms
MKT Research and Analysis- Ch. 13
43 terms
Other sets by this creator
Loop
18 terms
HRM Certification
51 terms
Cintas Item Codes
33 terms
Cintas Color Codes
16 terms
Verified questions
finance
Which one of the following would *not* be considered in the development of a partnership agreement? A. profit and loss levels B. processing disputes C. stock options D. asset contributions
world geography
What are the general climate conditions in much of Eastern Europe?
economics
The Hatfield family lives on the east side of the Hatatoochie River and the McCoy family lives on the west side. Each family's diet consists of fried chicken and corn on the cob, and each is self-sufficient, raising its own chickens and growing its own corn. Assume the Hatfield family has a comparative advantage in the production of corn. Which family has the comparative advantage in the production of chickens? Explain.
algebra
*Find the absolute maximum and absolute minimum on the given interval.* $$ p(x)=\sqrt[3]{x}\ \text{on}\ [-125,216] $$
Recommended textbook solutions
Marketing Essentials: The Deca Connection
1st Edition
Carl A. Woloszyk, Grady Kimbrell, Lois Schneider Farese
1,600 solutions
Marketing Essentials
4th Edition
Grady Kimbrell
2,121 solutions
Consumer Behavior: Buying, Having, and Being
12th Edition
Michael R Solomon
485 solutions
Advertising and Promotion: An Integrated Marketing Communications Perspective
11th Edition
George Belch, Michael Belch
214 solutions
Other Quizlet sets
Geo Test 4
56 terms
Software Development Module 2
27 terms
BIO 213 Midterm
74 terms