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Chapter 12
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Terms in this set (46)
Types of Statistical Analyses
-Descriptive analysis
-Inferential analysis
-Differences analysis
-Associative analysis
-Predictive analysis
Descriptive Statistics
used by marketing researchers to DESCRIBE THE SAMPLE DATASET in such a way as to portray the "typical" respondent and to reveal the general pattern of responses
Inference Analysis
used when marketing researchers use statistical procedures to generalize the results of the sample to the target population it represents
Difference Analysis
used to determine the degree to which real and generalizable differences exist in the population to help the manager make an enlightened decision on which advertising theme to use.
Association Analysis
investigates if and how two variables are related
Predictive Analysis
Statistical procedures and models to help make forecasts about future events
Descriptive Statistics (description)
summarizes basic findings for the sample
Descriptive Statistics
(example)
Describes the typical respondent, describes how similar respondents are to the typical respondent
Descriptive Statistics
(statistical concepts)
Mean, Median, mode frequency distribution, range, standard deviation
Inference Analysis
(description)
determines population parameters, test hypotheses
Inference Analysis (example)
estimates population values
Inference Analysis (Statistical concepts)
Standard error, null hypothesis
Difference analysis (description)
determines if differences exist, evaluates statistical significance or difference in the means of two groups in a sample
Difference Analysis (example)
Evaluates the statistical significance of difference in the means of two groups in a sample
Difference Analysis (Statistical concepts)
t test of differences, analysis of variance
Association Analysis
(description)
Determines connections
Association Analysis (Example)
determines if two variables are related in a systematic way
Association Analysis (Statistical Concepts)
Correlation, cross-tabulation
Predictive Analysis (Description)
finds complex relationships for variables in the dataset
Predictive Analysis (Example)
determines how several independent variables influence a key dependent variable
Predictive Analysis (Statistical Concepts)
Multiple regression
Data via Descriptive Analysis
two sets of measures are used extensively to describe the information obtained in a sample.
-central tendency
-variability
Central Tendency
measures the data that describe the "typical" respondent or response
Variability
measures that describe how similar (dissimilar) respondents responses are to (from) "typical" respondents or responses
Central Tendency "Typical" Respondent
-basic data analysis goal involved in all measures of central tendency is to report a single piece of information that describes the most typical response to a question
-applies to any statistical measure used that somehow reflects a typical or frequent response
Measures of Central Tendency
Mode
Median
Mean
Mode
-nominal
-a descriptive analysis measure defined as the value in a string of numbers that occurs most often
Median
-ordinal
-expresses that value whose occurrence lies in the middle of an ordered set of values
Mean
-Interval/ratio
-avearge
Measures of Variability
All measures of variability are concerned with depicting the "typical" difference between the values in a set of values
-Frequency Distribution
-Range
-Standard Deviation
Frequency Distribution
- nominal or ordinal
-count or % of every possible response
-a tabulation of the number of times that each different value appears in a
particular set of values
-conversion is accomplished simply through quick division of the frequency for each value by the total number of observations for all values, resulting in a percent, called a percentage distribution
Range
-lowest or highest
-identifies the distance between lowest value (minimum) and the highest value (maximum) in an ordered set of values
Standard Deviation
-interval or ratio
-standardized # to tell you how much variance you have on a nominal curve
-indicates the degree of variation or diversity in the values in such a way to be translatable into a normal or bell-shaped curve distribution
What is you gender?
-nominal scale
-mode
-frequency and/or percent distribution
Rank these five brands from your first choice to your fifth choice.
-ordinal scale
-median
-cumulative percentage distribution
On a scale of 1 to 5, how does Starbucks rate on variety of its coffee drinks
-Interval scale
-mean
-standard deviation and/or range
About how many times did you buy fast food for lunch last week
-ratio scale
-mean
-standard deviation and/ or range
Parameter estimation
the process of using sample information to compute an interval that describes the range of a parameter such as the population mean or the population percentage
-sample statistics
-standard error
-level of confidence
Sample Statistics and Population Parameters
STATISTICS
-values that are computed from information provided by a sample
Sample Statistics and population parameters
INFERENCE
a form of logic in which you make a general statement (a generalization) about an entire class based on what you have observed about a small set of members of that class
Sample Statistics and population parameters PARAMETERS
-values that are computed from a complete census which are considered to be precise and valid measures of the population
Sample Statistics and population parameters
STATISTICAL INFERENCE
-a set of procedures in which the sample size and sample statistic are used to make an estimate of the corresponding population parameter
-parameter estimate
-hypothesis testing
Parameter Estimate
used to approximate population value (parameter) through the use of confidence intervals
Hypothesis Testing
is used to compare the sample statistic with what is believed (hypothesized) to be the population value prior to undertaking the study
Sample Statistics and population parameters
-sample statistic is usually a MEAN or PERCENTAGE
-STANDARD ERROR is the measure of variability in the sampling distribution
-Confidence Interval- the degree of accuracy desired by the researcher stated in the form of a range with an upper an lower boundary
Hypothesis Tests
test of an hypothesized population parameter value
-test of an hypothesis about a PERCENT
-test of an hypothesis about a MEAN
-the crux of statistical hypothesis testing is the SAMPLE DISTRIBUTION
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