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by: Michelle H.

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# Stat 2000 Test 2 Study Guide Stat 2000

Marketplace > University of Georgia > Statistics > Stat 2000 > Stat 2000 Test 2 Study Guide
Michelle H.
UGA
GPA 4.0

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This study guide contains an explanation of all formulas and an explanation as to the proper way to solve problems using StatCrunch.
COURSE
Introductory Statistics
PROF.
TYPE
Study Guide
PAGES
4
WORDS
CONCEPTS
Statistics
KARMA
50 ?

## Popular in Statistics

This 4 page Study Guide was uploaded by Michelle H. on Wednesday October 12, 2016. The Study Guide belongs to Stat 2000 at University of Georgia taught by in Fall 2016. Since its upload, it has received 671 views. For similar materials see Introductory Statistics in Statistics at University of Georgia.

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Date Created: 10/12/16
Stat 2000 Test 2 Study Guide Important Formulas ● Checking for a normal distribution:​​np > ​ (1-p)>15 ○ n: Sample size ○ p: Sample proportion ● Standard error = Calculations with StatCrunch Confidence Interval for Proportions ● We’re going to use ​With Summary​ since we don’t have a data set in StatCrunch, we just have a numerical summary. ● If the problem gives you a full data set to load, then use ​With Data ● If the problem gives you a percentage, multiply it by the sample size to find the number of successes. ○ For example, if 75% of 100 students skip this class, the number of students would be (0.75)(100) = 75 students ● The default in StatCrunch isn’t Confidence Interval, so ​make sure to select it. ● The confidence interval is ​ . Limit to the U. Limit. ○ Here, it’s 0.381 to 0.519 Confidence Interval for Means ● Similar to when we calculated a confidence interval for a proportion, select ​With Summary unless a data set is provided. ● At this step, make sure you select ​Confidence Interval​ as it is not the default test . ● Again, the confidence interval is the ​L. Limit​ to the ​U. Limit. Probability​:​ A way of ​quantifying​ data that allows us to determine how likely something is to occur. ● Probability allows us to draw conclusions, or ​inferences,​ about the population. ○ Quantifying data is finding a number to represent a non-numerical observation, such as how often you will wear red on game days Random​:​ Occurs within a d ● We can use random data to draw conclusions based on the data Law of Large Numbers​:​ As the number of trials increases, the proportion will reach the “true” proportion of success ● Randomness is harder to predict in the short-term but easier to predict in the short term. Sample Space​:​ A listing of all possible outcomes ● Since a regular dice can only give you a number between 1 and 6, the sample space for rolling a dice is [1,2,3,4,5,6] Properties of Probability ● All probabilities are between 0 and 1 ○ Probabilities and proportions are always given in ​decimals​ ​unless otherwise stated ● The sum of all possible probabilities is always 1 ● A mutually exclusive (independent) pair of events are ​complements​ to each other ○ For example, If the probability of heads is 0.4, the probability of tails is 0.6 Independence Assumption:​ The sampled values must be independent of each other ​ Sample Size Assumption:​ The sample size, n representation of the data. Population Distribution:​ The distribution the sample was taken from Data Distribution:​ The distribution of the data from a single sample Sampling Distribution:​ The distribution of the averages from ​each​ sample.

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