Chapter 3: Central Tendency
Chapter 4: Variability
Chapter 5: z-Scores: Location of Scores and Standardized Distributions
Central tendency: measure of statistics to determine a single score that defines the middle of a distribution 
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Mean: sum of all of the scores divided by the number of scores; it is referred to as the average; it can be found using a formula. If one value is changed, the mean will also change. When the data is symmetric, the best measure to use is the mean.  [Link Title](https://www.youtube.com/watch?v=0io9U8Jcjeo)
median: midpoint of a distribution of scores listed in ascending order; it can be found for an open-ended distribution; it is appropriate for ordinal data; it can be found when there are underdetermind scores. The the data has a negative or positive skewness, the median is the best measure to use.
Mode: the number that occurs the most; there can be more than one mode. 
- Range is the difference between the highest and the lowest scores.
Bimodal: distribution with two modes
Line graph: display in which points connected by straight lines show several different means 
Formulas for Sample Variance and Standard Deviation
Biased and Unbiased Statistics
What is meant by a biased statistic? The average value for the statistic either overestimates or underestimates the corresponding population parameter.
What is an example of unbiased statistics? Both the sample mean and the sample variance (dividing by n − 1)
What is BIASED STATISTIC? It is a metric that consistently tends to overestimate (or underestimate) the population parameter
What is UNBIASED STATISTIC? It is a metric that provides an accurate estimate of the corresponding population parameter
Standard Deviation and Descriptive Statistics 
How is the standard deviation represented in a frequency distribution graph? By a horizontal line or arrow extending from the mean for a distance equal to one standard deviation.
What symbols are used for the mean and standard deviation for a sample in a research report? The mean is identified by the letter M and the standard deviation is represented by SD.
Variance is the mean squared deviation and is obtained by finding the sum of the squared deviations and then dividing by the number of scores. 
Standard Deviation for Population is the square root of the variance 
Sample standard deviation is the square root of the variance. 
A standard deviation of zero indicates there is no variability. In this case, all of the scores in the sample have exactly the same value.
For symmetrical distributions, the mean will equal the median. If there is only one mode, then it will have the same value, too. In a skewed distribution, the scores tend to pile up toward one end of the scale and taper off gradually at the other end. 
Attached is the formula for computing the z-score. Listed in individual branches are the 4 steps involved in computing the z-score. 
Step 1: Determine the sign of the z-score.
Step 4: Confirm the answer using the z-score formula; also, I used the normalcdf my Texas-Instrument 84-Plus calculator to confirm my z-scores.
Step 3: Combine the sign from Step 1 with the numerical value from Step 2.
Step 2: Convert the distance between X and mu into standard deviation units.
The relationship between z-score values and locations in a population distribution. 
Define Z-score: numerical value equal to the distance from the mean measured in standard deviations
What is z-score transformation? It is the changing raw scores into z-scores; process changes raw scores into standard scores with mean 0 and standard deviation 1
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