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- https://stats.libretexts.org/Bookshelves/Applied_Statistics/Introduction_to_Applied_Statistics_for_Psychology_Students_(Sarty)/14%3A_Correlation_and_Regression
- https://stats.libretexts.org/Bookshelves/Applied_Statistics/Introduction_to_Applied_Statistics_for_Psychology_Students_(Sarty)/12%3A_ANOVA/12.03%3A_SPSS_Lesson_8-_One-way_ANOVALook at the Model menu and leave the button selected to “Full Factorial” (for one-way ANOVA this is the only choice anyway) and leave the “Include intercept in model” button as selected too. The outpu...Look at the Model menu and leave the button selected to “Full Factorial” (for one-way ANOVA this is the only choice anyway) and leave the “Include intercept in model” button as selected too. The output is pretty much the same as before (the homogeneous subsets is output also but it is not shown here) but the ANOVA table is a little different.
- https://stats.libretexts.org/Bookshelves/Applied_Statistics/Introduction_to_Applied_Statistics_for_Psychology_Students_(Sarty)/05%3A_The_Normal_Distributions/5.03%3A_Normal_DistributionTo work with normal distribution, in particular so we can use the Standard Normal Distribution Table and the t Distribution Table in the Appendix, we need to transform it to the standard normal distri...To work with normal distribution, in particular so we can use the Standard Normal Distribution Table and the t Distribution Table in the Appendix, we need to transform it to the standard normal distribution using the \(z\)-transform. So we are looking for the probabilities \(P = P(z_{1} < x < z_{2})\) for an interval to the right of the mean or \(P = P(-z_{2} < x <- z_{1})\) for an interval to the left of the mean.
- https://stats.libretexts.org/Bookshelves/Applied_Statistics/Introduction_to_Applied_Statistics_for_Psychology_Students_(Sarty)/09%3A_Hypothesis_Testing/9.02%3A_z-Test_for_a_MeanThat is, for the case of means here, you know for sure that the mean of the population, if it is different from the null hypothesis mean, if greater than (or less than) the null hypothesis mean. In th...That is, for the case of means here, you know for sure that the mean of the population, if it is different from the null hypothesis mean, if greater than (or less than) the null hypothesis mean. In that case, if \(p < 0.05\) then it means that the data (the test statistic) indicates there is less than a 5% chance that the result is a statistical fluke; that there is less than a 5% chance that the decision is a Type I error.
- https://stats.libretexts.org/Bookshelves/Applied_Statistics/Introduction_to_Applied_Statistics_for_Psychology_Students_(Sarty)/10%3A_Comparing_Two_Population_Means/10.01%3A_Unpaired_z-TestFigure 10.1 : The distribution of the difference of sample means \(\bar{x}_{1} - \bar{x}_{2}\) under the null hypothesis \(H_{0}: \mu_{1} - \mu_{2} = 0\). Note that \(\bar{x}_{1} > \bar{x}_{2}\) (\(8....Figure 10.1 : The distribution of the difference of sample means \(\bar{x}_{1} - \bar{x}_{2}\) under the null hypothesis \(H_{0}: \mu_{1} - \mu_{2} = 0\). Note that \(\bar{x}_{1} > \bar{x}_{2}\) (\(8.6>7.9\)) so \(H_{1}:\mu_1 > \mu_2\) is true on the face of it. If \(H_{1}\) is not true on the face of it then \(H_{1}\) is just plain false without the need for any statistical test.
- https://stats.libretexts.org/Bookshelves/Applied_Statistics/Introduction_to_Applied_Statistics_for_Psychology_Students_(Sarty)/06%3A_Percentiles_and_Quartiles/6.02%3A_Finding_Outliers_Using_QuartilesWe can use quartiles to identify outliers or data points that are wildly discrepant with the rest of the data. With the IQR any data value that satisfies: This is one of many ways one can define an ou...We can use quartiles to identify outliers or data points that are wildly discrepant with the rest of the data. With the IQR any data value that satisfies: This is one of many ways one can define an outlier. As we will discuss below, it is a robust way of identifying outliers. \[ Q_{1} = 9 \hspace{.25in} Q_{2} = 14 \hspace{.25in} Q_{3} = 20 \] \[ IQR = Q_{3} - Q_{1} = 20 - 9 = 11. \] Following our rules for finding outliers, we compute:
- https://stats.libretexts.org/Bookshelves/Applied_Statistics/Introduction_to_Applied_Statistics_for_Psychology_Students_(Sarty)/10%3A_Comparing_Two_Population_Means/10.08%3A_Confidence_Intervals_for_Paired_t-TestsThe usual form applies : \[\overline{D} - E < \mu_{D} < \overline{D} + E\] where now \[E = t_{\cal{C}} \left( \frac{s_D}{\sqrt{n}}\right)\] and \(t_{\cal{C}}\) can found from the t Distribution Table ...The usual form applies : \[\overline{D} - E < \mu_{D} < \overline{D} + E\] where now \[E = t_{\cal{C}} \left( \frac{s_D}{\sqrt{n}}\right)\] and \(t_{\cal{C}}\) can found from the t Distribution Table in the \(\nu = n -1\) line using the “confidence intervals” heading.
- https://stats.libretexts.org/Bookshelves/Applied_Statistics/Introduction_to_Applied_Statistics_for_Psychology_Students_(Sarty)/14%3A_Correlation_and_Regression/14.02%3A_CorrelationStandard warning about correlation and causation : If you find that \(x\) and \(y\) are highly correlated (i.e. \(r\) is close to \(+1\) or \(-1\)) then you cannot say that \(x\) causes \(y\) or that ...Standard warning about correlation and causation : If you find that \(x\) and \(y\) are highly correlated (i.e. \(r\) is close to \(+1\) or \(-1\)) then you cannot say that \(x\) causes \(y\) or that \(y\) causes \(x\) or that there is and causal relationship between \(x\) and \(y\) at all.
- https://stats.libretexts.org/Bookshelves/Applied_Statistics/Introduction_to_Applied_Statistics_for_Psychology_Students_(Sarty)/03%3A_Descriptive_Statistics-_Central_Tendency_and_Dispersion
- https://stats.libretexts.org/Bookshelves/Applied_Statistics/Introduction_to_Applied_Statistics_for_Psychology_Students_(Sarty)/08%3A_Confidence_Intervals
- https://stats.libretexts.org/Bookshelves/Applied_Statistics/Introduction_to_Applied_Statistics_for_Psychology_Students_(Sarty)/00%3A_Front_Matter/01%3A_TitlePageIntroduction to Applied Statistics for Psychology Students (Sarty)
