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Statistics By Jim

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interpreting results

Monte Carlo Simulation: Make Better Decisions

By Jim Frost 2 Comments

What is Monte Carlo Simulation?

Monte Carlo simulation uses random sampling to produce simulated outcomes of a process or system. This method uses random sampling to generate simulated input data and enters them into a mathematical model that describes the system. The simulation produces a distribution of outcomes that analysts can use to derive probabilities. [Read more…] about Monte Carlo Simulation: Make Better Decisions

Filed Under: Probability Tagged With: analysis example, distributions, Excel, interpreting results

Principal Component Analysis Guide & Example

By Jim Frost Leave a Comment

What is Principal Component Analysis?

Principal Component Analysis (PCA) takes a large data set with many variables per observation and reduces them to a smaller set of summary indices. These indices retain most of the information in the original set of variables. Analysts refer to these new values as principal components. [Read more…] about Principal Component Analysis Guide & Example

Filed Under: Basics Tagged With: analysis example, choosing analysis, conceptual, interpreting results, multivariate

Z Test: Uses, Formula & Examples

By Jim Frost Leave a Comment

What is a Z Test?

Use a Z test when you need to compare group means. Use the 1-sample analysis to determine whether a population mean is different from a hypothesized value. Or use the 2-sample version to determine whether two population means differ. [Read more…] about Z Test: Uses, Formula & Examples

Filed Under: Hypothesis Testing Tagged With: analysis example, assumptions, choosing analysis, interpreting results

Linear Regression Equation Explained

By Jim Frost 2 Comments

A linear regression equation describes the relationship between the independent variables (IVs) and the dependent variable (DV). It can also predict new values of the DV for the IV values you specify. [Read more…] about Linear Regression Equation Explained

Filed Under: Regression Tagged With: analysis example, interpreting results

Relative Risk: Definition, Formula & Interpretation

By Jim Frost Leave a Comment

What is Relative Risk?

Relative risk is the ratio of the probability of an adverse outcome in an exposure group divided by its likelihood in an unexposed group. This statistic indicates whether exposure corresponds to increases, decreases, or no change in the probability of the adverse outcome. Use relative risk to measure the strength of the association between exposure and the outcome. Analysts also refer to this statistic as the risk ratio. [Read more…] about Relative Risk: Definition, Formula & Interpretation

Filed Under: Probability Tagged With: analysis example, interpreting results

Factor Analysis Guide with an Example

By Jim Frost 3 Comments

What is Factor Analysis?

Factor analysis uses the correlation structure amongst observed variables to model a smaller number of unobserved, latent variables known as factors. Researchers use this statistical method when subject-area knowledge suggests that latent factors cause observable variables to covary. Use factor analysis to identify the hidden variables. [Read more…] about Factor Analysis Guide with an Example

Filed Under: Basics Tagged With: analysis example, conceptual, interpreting results, multivariate

What is K Means Clustering? With an Example

By Jim Frost 10 Comments

What is K Means Clustering?

The K means clustering algorithm divides a set of n observations into k clusters. Use K means clustering when you don’t have existing group labels and want to assign similar data points to the number of groups you specify (K). [Read more…] about What is K Means Clustering? With an Example

Filed Under: Basics Tagged With: analysis example, interpreting results

Cronbach’s Alpha: Definition, Calculations & Example

By Jim Frost 4 Comments

What is Cronbach’s Alpha?

Cronbach’s alpha coefficient measures the internal consistency, or reliability, of a set of survey items. Use this statistic to help determine whether a collection of items consistently measures the same characteristic. Cronbach’s alpha quantifies the level of agreement on a standardized 0 to 1 scale. Higher values indicate higher agreement between items. [Read more…] about Cronbach’s Alpha: Definition, Calculations & Example

Filed Under: Basics Tagged With: analysis example, conceptual, interpreting results

Chi-Square Goodness of Fit Test: Uses & Examples

By Jim Frost 4 Comments

The chi-square goodness of fit test evaluates whether proportions of categorical or discrete outcomes in a sample follow a population distribution with hypothesized proportions. In other words, when you draw a random sample, do the observed proportions follow the values that theory suggests. [Read more…] about Chi-Square Goodness of Fit Test: Uses & Examples

Filed Under: Hypothesis Testing Tagged With: analysis example, conceptual, distributions, interpreting results

Inter-Rater Reliability: Definition, Examples & Assessing

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What is Inter-Rater Reliability?

Inter-rater reliability measures the agreement between subjective ratings by multiple raters, inspectors, judges, or appraisers. It answers the question, is the rating system consistent? High inter-rater reliability indicates that multiple raters’ ratings for the same item are consistent. Conversely, low reliability means they are inconsistent. [Read more…] about Inter-Rater Reliability: Definition, Examples & Assessing

Filed Under: Hypothesis Testing Tagged With: analysis example, conceptual, interpreting results

Margin of Error: Formula and Interpreting

By Jim Frost Leave a Comment

What is the Margin of Error?

The margin of error (MOE) for a survey tells you how near you can expect the survey results to be to the correct population value. For example, a survey indicates that 72% of respondents favor Brand A over Brand B with a 3% margin of error. In this case, the actual population percentage that prefers Brand A likely falls within the range of 72% ± 3%, or 69 – 75%. [Read more…] about Margin of Error: Formula and Interpreting

Filed Under: Hypothesis Testing Tagged With: conceptual, interpreting results

Confidence Intervals: Interpreting, Finding & Formulas

By Jim Frost 2 Comments

What is a Confidence Interval?

A confidence interval (CI) is a range of values that is likely to contain the value of an unknown population parameter. These intervals represent a plausible domain for the parameter given the characteristics of your sample data. Confidence intervals are derived from sample statistics and are calculated using a specified confidence level. [Read more…] about Confidence Intervals: Interpreting, Finding & Formulas

Filed Under: Hypothesis Testing Tagged With: conceptual, interpreting results

Test Statistic: Definition, Types & Formulas

By Jim Frost 1 Comment

What is a Test Statistic?

A test statistic assesses how consistent your sample data are with the null hypothesis in a hypothesis test. Test statistic calculations take your sample data and boil them down to a single number that quantifies how much your sample diverges from the null hypothesis. As a test statistic value becomes more extreme, it indicates larger differences between your sample data and the null hypothesis. [Read more…] about Test Statistic: Definition, Types & Formulas

Filed Under: Hypothesis Testing Tagged With: conceptual, interpreting results

Odds Ratio: Formula, Calculating & Interpreting

By Jim Frost 4 Comments

What is an Odds Ratio?

An odds ratio (OR) calculates the relationship between a variable and the likelihood of an event occurring. A common interpretation for odds ratios is identifying risk factors by assessing the relationship between exposure to a risk factor and a medical outcome. For example, is there an association between exposure to a chemical and a disease? [Read more…] about Odds Ratio: Formula, Calculating & Interpreting

Filed Under: Probability Tagged With: conceptual, interpreting results

Case Control Study: Definition, Benefits & Examples

By Jim Frost 2 Comments

What is a Case Control Study?

A case control study is a retrospective, observational study that compares two existing groups. Researchers form these groups based on the existence of a condition in the case group and the lack of that condition in the control group. They evaluate the differences in the histories between these two groups looking for factors that might cause a disease. [Read more…] about Case Control Study: Definition, Benefits & Examples

Filed Under: Basics Tagged With: conceptual, experimental design, interpreting results

5 Number Summary: Definition, Finding & Using

By Jim Frost 6 Comments

What is the 5 Number Summary?

The 5 number summary is an exploratory data analysis tool that provides insight into the distribution of values for one variable. Collectively, this set of statistics describes where data values occur, their central tendency, variability, and the general shape of their distribution. [Read more…] about 5 Number Summary: Definition, Finding & Using

Filed Under: Basics Tagged With: analysis example, distributions, interpreting results

Variance: Definition, Formulas & Calculations

By Jim Frost 2 Comments

Variance is a measure of variability in statistics. It assesses the average squared difference between data values and the mean. Unlike some other statistical measures of variability, it incorporates all data points in its calculations by contrasting each value to the mean. [Read more…] about Variance: Definition, Formulas & Calculations

Filed Under: Basics Tagged With: conceptual, distributions, interpreting results

Mean Squared Error (MSE)

By Jim Frost Leave a Comment

Mean squared error (MSE) measures the amount of error in statistical models. It assesses the average squared difference between the observed and predicted values. When a model has no error, the MSE equals zero. As model error increases, its value increases. The mean squared error is also known as the mean squared deviation (MSD). [Read more…] about Mean Squared Error (MSE)

Filed Under: Regression Tagged With: conceptual, interpreting results

Paired T Test: Definition & When to Use It

By Jim Frost 5 Comments

What is a Paired T Test?

Use a paired t-test when each subject has a pair of measurements, such as a before and after score. A paired t-test determines whether the mean change for these pairs is significantly different from zero. This test is an inferential statistics procedure because it uses samples to draw conclusions about populations.

Paired t tests are also known as a paired sample t-test or a dependent samples t test. These names reflect the fact that the two samples are paired or dependent because they contain the same subjects. Conversely, an independent samples t test contains different subjects in the two samples. [Read more…] about Paired T Test: Definition & When to Use It

Filed Under: Hypothesis Testing Tagged With: analysis example, assumptions, choosing analysis, interpreting results

Independent Samples T Test: Definition, Using & Interpreting

By Jim Frost 3 Comments

What is an Independent Samples T Test?

Use an independent samples t test when you want to compare the means of precisely two groups—no more and no less! Typically, you perform this test to determine whether two population means are different. This procedure is an inferential statistical hypothesis test, meaning it uses samples to draw conclusions about populations. The independent samples t test is also known as the two sample t test. [Read more…] about Independent Samples T Test: Definition, Using & Interpreting

Filed Under: Hypothesis Testing Tagged With: analysis example, assumptions, choosing analysis, interpreting results

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    Top Posts

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