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| an analysis technique applying mathematics (suffers from the unobserved)Intervals w/o natural zero ratio with natural zero |
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| an inquiry into the reasoning behind human behavior |
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| likelihood of some event occurring |
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| Collecting, gathering, organizing data analyzing data from a sample and generalize it to a population |
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| is a variable whose value is subject to variations due to chance |
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| Cumulative distribution function |
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The cumulative distribution function of a real-valued random variable X is the function given by
where the right-hand side represents the probability that the random variable X takes on a value less than or equal to x. The probability that X lies in the semi-closed interval (a, b], where a < b, is therefore |
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| Probability distribution function |
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| Dummy variable – type of variable known as nominal or categorical variable 0,1 |
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| Difference between the actual value of a quantity and the value obtained by a measurement. |
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| experimental group/treatment group |
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| An experimental group is the group in a scientific experiment where the experimental procedure is performed. This group is exposed to the independent variable being tested and the changes observed and recorded. |
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| A control group is a group separated from the rest of the experiment where the independent variable being tested cannot influence the results. |
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| represents all the members of a group/interest |
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| A collection of cases selected from a larger population |
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| Variables measured with numerical values where the numbers are meaningful (2 is larger than 1) but the distance between the numbers is not constant. |
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| labels are used to identify different levels of a variable |
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| Variables measured with numerical values with equal distance, or space between each number (e.g. 2 is twice as much as 1, 4 is twice as much as 2, the distance between 1 and 2 is the same as the distance between 2 and 3) |
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| when variables are measured using categories or names (dummy) |
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| cannot take on all values within the limits of the variable |
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| Variables that can take on any value |
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| A mutual relationship or connection between two or more variables |
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| the concept that variation in one variable causes variation in another variable |
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| refers to multi-dimensional data frequently involving measurements over time |
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| Cross-sectional data refers to data collected by observing many subjects (such as individuals, firms or countries/regions) at the same point of time, or without regard to differences in time. |
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| s a sequence of data points, measured typically at successive points in time spaced at uniform time intervals. |
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| A distribution that has one value that have the highest frequency of scores |
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| A bell-shaped frequency distribution of scores that has the mean, median, and mode in the middle of the distribution and is symmetrical and asymmetrical |
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| Negative distribution/ Skew |
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| In a skewed distribution, when most of the scores are clustered at the higher end of the distribution with fewer scores creating a tail at the lower end of the distribution |
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| A distribution that has two values that have the highest frequency of scores |
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| is a discrete probability distribution that expresses the probability of a given number of events occurring in a fixed interval of time and/or space if these events occur with a known average rate and independently of the time since the last event.[1] The Poisson distribution can also be used for the number of events in other specified intervals such as distance, area or volume |
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| The arithmetic average of a distribution of scores- add up all the numbers, then divide by how many numbers there are.- adding/summing all the scores in a distribution and dividing by the number of scores |
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| The score in a distribution that marks that 50th percentile. It is the score at which 50% of the distribution falls below and above 50% fall above |
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| The score in the distribution that occurs most frequently Score that appears the most/ most common score, most frequent |
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| The sum of the squared deviations divided by the number of cases in the population, or by the number of cases minus one in the sample |
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| The difference between the largest score and the smallest score of a distribution |
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| Tells us about variation (the larger the sample the smaller the Standard Deviation) |
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| When a distribution of scores has a high # of scores clustered at one end of the distribution with relatively few scores spread out toward the other end of the distribution, forming a tail |
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| The shape of a distribution of scores in terms of its flatness or peakedness |
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| Extreme scores that are more than two standard deviations above or below the mean |
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| is a theorem that describes the result of performing the same experiment a large number of times. According to the law, the average of the results obtained from a large number of trials should be close to the expected value, and will tend to become closer as more trials are performed. |
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| s long as you have a reasonably large sample size (n=30) the sampling distribution of the mean will be normally distributed, even if the distribution of scores in your score sample is not. |
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| An interval calculated using sample statistics to contain the population parameter |
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| # that indicates how far above or below the mean a given score in the distribution is in standard deviation units. z=individual score-mean/sd |
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| Positive Distribution/ Skew |
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| In a skewed distribution, when most of the scores are clustered at the lower end of the distribution with a few scores creating a tail at the higher end of the distribution |
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| apply only to the members of the sample or the population from data collection |
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| Statistics, derived from sample data, that are used to make inferences about the population from which the sample was drawn |
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