Descriptive & Inferential Statistics + Ch12 Flashcards
Mode
The most frequent value
Median
The middle value
Mean
The average value
Normal distribution
Most data points cluster to the center of the distribution
Central tendency
Describe the typical respondent or values in a dataset
Measures of central tendency
Mode (nominal, ordinal, interval, ratio)
Median (ordinal, interval, ratio)
Mean (interval, ratio)
Measures of dispersion
Range (interval, ratio)
Standard deviation (interval, ratio)
Dispersion
How spread out the data is
Range
The spread of data, identifies the highest and lowest values (range is the difference between)
Standard deviation
Dispersion around the mean
Statistics
Are sample values
Parameters
Are estimated population values
Statistical inference
Is the estimation of population values by analyzing the sample
Two types of statistics inference
Parameter estimation technique (eg: confidence intervals)
Hypothesis testing
Parameter estimation
Coming up with an interval where the true population parameter lies
Confidence level
How sure you are of the results you are reporting
eg: confidence level of 95% = 95/100 of your sample will be between the intervals determined by your margin of error
3 components of data preparation (ECC)
Data entry
Data coding
Data cleaning
Data entry
Convert data to electronic form (if needed)
Data coding
Group and assign numeric codes to qualitative responses
Data cleaning
Check for errors & inconsistencies
Point estimate
The result % without the error margin
Margin of error
+ or - % from the point estimate
Confidence interval
The point estimate with the margin of error gives you the range of the confidence interval
eg: 10% PE with a MoE of +/-1% = CI of 9-11%
Null hypothesis (H0)
Indicates no correlation between variables
Alternative hypothesis (Ha/H1)
There is a relationship between variables
Statistical significance
P-value < 0.05 = Significant
<0.05 = reject null
Descriptive analysis
Used to describe the variables in a dataset
Inference analysis
Used to generate conclusions about a population’s characteristics based on sample data
Difference analysis
Used to compare the mean of the responses of one group to that of another group
Association analysis
Determines the strength and direction of relationships between two or more variables
Relationships analysis
Allows insights into multiple relationships among variables