Priyanka Thakur

Simple linear regression   Purpose: To determine the relationship between IV and DV and to predict the value of the dependent variable (Y) based on the value of the independent variable (X). Requirement: Scales of measurement for variables: DV -interval or ratio IV -interval or ratio Descriptive : Steps: ·         Create a scatter plot graph to identify the line of best life, the purpose of the identity the trend of the data and predict the future. ·         Identify the intercept and slope of the regression line. Inferential steps: Steps: 1.       Conduct F test to predict equation and t-test to test the slop. Th purpose of the test is to find a relationship between IV and DV. 2.       Identify IV and DV 3.       State HO and HA 4.       Set ...

Analysis of Variance (ANOVA)  is a statistical method used to test differences between two or more means. It may seem odd that the technique is called “Analysis of Variance” rather than “Analysis of Means.” As you will see, the name is appropriate because inferences about means are made by analyzing variance. ANOVA is used to test general rather than specific differences among means. This can be seen best by example. In the case study “Smiles and Leniency,” the effect of different types of smiles on the leniency shown to a person was investigated. Four different types of smiles (neutral, false, felt, miserable) were investigated. The chapter “All Pairwise Comparisons among Means” showed how to test differences among means. The results from the Tukey HSD test are shown in Table 1. Table 1. Six Pairwise Comparisons. Comparison M i -M j Q p False – Felt 0.46 1.65 0.649 False – Miserable...

T-tests are handy hypothesis tests in statistics when you want to compare means. You can compare a sample mean to a hypothesized or target value using a one-sample t-test. You can compare the means of two groups with a two-sample t-test. If you have two groups with paired observations (e.g.before and after measurements), use the paired t-test. How do t-tests work? How do t-values fit in? In this series of posts, I’ll answer these questions by focusing on concepts and graphs rather than equations and numbers. After all, a key reason to use statistical software like Minitab is so you don’t get bogged down in the calculations and can instead focus on understanding your results. calculate probabilities and assess hypotheses. What are T-Tests? Sometimes, we don’t just look at or describe one group of data. Instead, we want to look at two groups of data and compare them. We want to see if the two groups are different. T-tests are often used to compare the means from ...

Hypothesis refers means an educated guess or an assumption that can be tested. Forms of hypothesis: 1.       HA- Research hypothesis 2.       HO- Null hypothesis HA: Research hypothesis: The research hypothesis is also known as an alternate hypothesis. HA is a statement on which a statistical hypothesis test is set up. HO- Null hypothesis: Null hypothesis is also known as no difference or no relationship, this is used to facilitate testing of research hypothesis Types of hypothesis: ·         One-tailed- Directional HA: p>0 HO: p<0 ·         Two-tailed- non-directional HA: p not equal to zero Types of Error: Type 1 Error – Rejection of a true Null hypothesis. Type 2 Error – It may have a false negative finding. Type 1 error is donated by Alpha. Level of significant can reduce...

Probability  is the measure of the likelihood that an event will occur. Probability is quantified as a number between 0 and 1, where, loosely speaking,0 indicates impossibility and 1 indicates certainty. The higher the probability of an event, the more likely it is that the event will occur. A simple example is the tossing of a fair (unbiased) coin. Since the coin is fair, the two outcomes (“heads” and “tails”) are both equally probable; the probability of “heads” equals the probability of “tails”; and since no other outcomes are possible, the probability of either “heads” or “tails” is 1/2 (which could also be written as 0.5 or 50%). Types of probability 1) Classical Probability – All sample points have equal chances of the event to happen. 2) Relative frequency- The probability of single data compared to the whole data i.e. possible event to happen relative to all the possible outcomes. 3) Subjectable -Individual, personal judgment to say the probability of t...

Normal Distribution : The normal curve is continuous, symmetrical, unimodal, have a bell-shaped form and mean, median and mode is equal. The distribution contains an infinite number of cases.  Normal distribution differs by mean and standard deviation. Z score: Z score tells us how many standard deviations a data value is above or below the mean for a specific distribution of values. If the standard deviation is zero, then the data value is the same as the mean. Standard Normal curve : A normal curve with mean zero and standard deviation 1 is called a standard normal curve.   Central limit theorem : As the sample size n increases without limit, the sample mean is considered instead of the population mean.