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Question: Head Start is a program for preschool children from educationally disadvantaged environments. Head Start began in 1964, and the data that follow are representative of early research on the effects of the program.
A group of children participated in the program and then enrolled in first grade. They took an IQ test, the national norm of which was 100. Perform a t test on the data. Begin your work by stating the null hypothesis and end by writing a conclusion about the effects of the program.
∑X = 5250 ∑X2 =560,854 N = 50
Development of objective(s) and research question(s): relevance with respect to the selected data set and Justification of selected statistical methods for analysis.
Sampling distribution for the sample mean for a sample of size 160. This looks even more like a normal curve. This looks almost exactly like a normal curve. What have we seen from the above example?
A researcher finds a correlation between self esteem and shyness is -0.50, which is significant for his sample. Interpret this correlation.
set up a hypothesis test for a scenario of your choice. you can use a scenario from your own experience or invent
If the observed value is 4, the expected value is 5, and the standard error is 4, then the test value used for the z test is equal to
If PV is a tree before observing the evidence, then we would like PV PV |A= a to be also represented as a tree. Is this always possible?
What is the difference between a repeated measures ANOVA and a mixed-design ANOVA?
Identify a healthcare problem that can be studied statistically. Formulate hypotheses based on a healthcare problem. Complete an ANOVA test using SPSS
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A computer repair service found that a random sample of 45 repair costs had a mean cost of $659. Assume that the population standard deviation is $125. Calculate the margin of error, E, for a 95% confidence interval for the population mean µ.
Find the values of the correlation coefficient and the coefficient of determination. Find the slope, y-intercept, and equation of the regression line for the data. Plot the points and a graph of the regression line on the same axes.
Find the mean, median, mode, standard deviation, and variance. Analyze and explain why these statistical measures are important and how they apply to your data.
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