________: which depends on how chi- square is being used.
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Ho
________: The distributions of the two populations are the same.
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Homogeneity
________: decides if two populations with unknown distributions have the same distribution as each other.
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Alternative Hypothesis
________ (Ha): A statement that we are trying to find evidence to support; contradictory to H0.
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Unbiasedness
________: the idea that a statistic is expected to give values centered on the unknown parameter value.
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Conclusion
________: conclude your results based on your interval with context.
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Ha
________: The distributions of the two populations are not the same.
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margin of error
The ________: how many percentages points your results will differ from the real population value.
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Independence
________: decides whether two variables are ________ or dependent.
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Bias
________: the difference between the estimated probability and the true value of the parameter being estimated.
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measurements
Two ________ (samples) are drawn from the same pair of individuals or objects.
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Correlation coefficient
________ (r): is numerical and provides a measure of strength and direction of the linear association between the independent variable x and the dependent variable y.
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Left
________- tailed tests:"too small "values of the statistic as compared to the hypothesized parameter value lead to the rejection of the null hypothesis.
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Parameter
________: characteristic of a population.
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Inefficiency
________: indicates that our guess is wrong unsystematically.
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test statistic
The ________: Measures the difference between the sample result and the null value.
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RR
Rejection or critical region (________ or CR): the set of test statistic values for which we should reject the null hypothesis.
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GOF
Ha for ________: The population does not fit the given distribution.
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Large test statistic
________: Observed values and corresponding expected values are not close to each other.
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Estimation process
________: procedure of guessing an unknown parameter value using the observed values from samples.
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Null Hypothesis
________ (H0): A statement of no change, no effect, or no difference.
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Parameter of interest
________: state what it is you are interested in with the context.
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Confidence interval
________: an interval estimate for an unknown population parameter.
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Hypothesis
________: a statement (or claim) about a property /characteristic of a population.
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Ho of Homogeneity
________: The two populations follow the same distribution.
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Parameter
characteristic of a population
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Statistic
number computed from the sample
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Estimation process
procedure of guessing an unknown parameter value using the observed values from samples
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Estimate
specific guess or value computed from a sample
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Point estimate
a single number computed from a sample and used to estimate a population parameter
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The margin of error
how many percentages points your results will differ from the real population value
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Unbiasedness
the idea that a statistic is expected to give values centered on the unknown parameter value
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Bias
the difference between the estimated probability and the true value of the parameter being estimated
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Variability
the degree of variation in statistics values
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Inefficiency
indicates that our guess is wrong unsystematically
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Confidence interval
an interval estimate for an unknown population parameter
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Confidence level
considered the probability that the calculated confidence interval estimate will contain the true population parameter
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Confidence level interpretation
"We estimate with % confidence that the true population mean (include the context of the problem) is between and (include appropriate units)."
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Hypothesis
a statement (or claim) about a property/characteristic of a population
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Hypothesis testing
a procedure, based on sample evidence and probability, for testing claims about a property/characteristic of a population
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Null Hypothesis (H0)
A statement of no change, no effect, or no difference
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Alternative Hypothesis (Ha)
A statement that we are trying to find evidence to support; contradictory to H0
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The test statistic
Measures the difference between the sample result and the null value
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p-value
the probability that, if the null hypothesis is true, the results from another randomly selected sample will be as extreme or more extreme as the results obtained from the given sample
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Large p-value
calculated from the data indicates that we should not reject the null hypothesis
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Smaller the p-value
the more unlikely the outcome, and the stronger the evidence is against the null hypothesis
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Type I error
We reject the null hypothesis when the null hypothesis is true
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α
P(Type I Error) = P(Rejecting H0 when H0 is true)
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Type II error
We do not reject the null hypothesis when the alternative hypothesis is true
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β
P(Type II Error) = P(Failing to Reject H0 when H0 is false)
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Left-tailed tests
"too small" values of the statistic as compared to the hypothesized parameter value lead to the rejection of the null hypothesis
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Right-tailed tests
"too-large" values of the statistic as compared to the hypothesized parameter a=value lead to the rejection of the null hypothesis
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Rejection or critical region (RR or CR)
the set of test statistic values for which we should reject the null hypothesis
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Non-rejection region
the set of test statistic values for which we should fail to reject the null hypothesis
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Critical value (CV)
the value of a test statistic that gives the boundary between the rejection and the non-rejection region
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Parameter of interest
state what it is you are interested in with the context
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Assumptions and conditions
check them fro the proper interval you are about to use
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Name the type of interval
state the name of the interval that youre about to set up
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Interval
perform your calculations and set up the interval
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Conclusion
conclude your results based on your interval with context
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p-value ≤ α
we reject the null hypothesis
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p-value > α
we fail to reject the null hypothesis
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If you Reject H0
There is sufficient evidence to conclude [statement in Ha]
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If you Fail to Reject H0
There is not sufficient evidence to conclude [statement in Ha]
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Parameter of interest
state what it is you are interested in with the context
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Hypothesis
State your null and alternative hypothesis
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Least-Squares Line
You have a set of data whose scatter plot appears to "fit" a straight line
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Least-squares regression line
Helps obtain a line of best fit
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Absolute value of a residual
measures the vertical distance between the actual value of y and the estimated value of y
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Slope equation
b = r (sy / sx)
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Interpretation of the Slope
"The slope of the best-fit line tells us how the dependent variable (y) changes for every one unit increase in the independent (x) variable, on average."
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Correlation coefficient (r)
is numerical and provides a measure of strength and direction of the linear association between the independent variable x and the dependent variable y
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Degrees of freedom
which depends on how chi-square is being used
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Population mean
μ = df
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The null and alternative hypotheses for GOF
may be written in sentences or may be stated as equations or inequalities
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Null hypothesis
The observed values of the data values and expected values are values you would expect to get
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Large test statistic
Observed values and corresponding expected values are not close to each other
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Expected value rule
Needs to be above 5 to be able to use the test
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Test of independence
Determines whether two factors are independent or not
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The null hypothesis for independence
states that the factors are independent
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The alternative hypothesis for independence
states that they are not independent (dependent)
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Expected value formula
(row total)(column total) / total number surveyed
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Test for Homogeneity
used to draw a conclusion about whether two populations have the same distribution
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Goodness-of-Fit
decides whether a population with an unknown distribution "fits" a known distribution
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Ho for GOF
The population fits the given distribution
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Ha for GOF
The population does not fit the given distribution
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Independence
decides whether two variables are independent or dependent
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Ho for Independence
The two variables (factors) are independent
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Ha for Independence
The two variables (factors) are dependent
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Homogeneity
decides if two populations with unknown distributions have the same distribution as each other
Studied by 11 people
Note 
Flashcard (34)