What is interval estimation in inferential statistics?
- The process of calculating the standard deviation of a population
- The process of determining the mode of a population
- The process of estimating the mean of a population
- The process of providing a range of values for an unknown population parameter
Interval estimation in inferential statistics is a method by which a range of values is provided that is likely to contain the population parameter. Instead of a single value, it provides an interval of estimates making it more flexible and informative than point estimation.
What is the primary goal of random sampling?
- To always select the same individuals
- To ensure that every member of the population has an equal chance of being selected
- To select individuals who are likely to give the desired results
- To select the individuals who are easiest to reach
The primary goal of random sampling is to ensure that every member of the population has an equal chance of being selected. This helps to reduce bias and increase the likelihood that the sample is representative of the population, which makes the results more valid and generalizable.
_______ regression is a method used to handle multicollinearity by adding a degree of bias to the regression estimates.
- Logistic
- Polynomial
- Ridge
- Simple linear
Ridge regression handles multicollinearity by introducing a degree of bias to the regression estimates, reducing their variance, and making them more reliable.
What are the assumptions made by the Spearman’s Rank Correlation test?
- The data is continuous and the relationship is monotonic
- The data is normally distributed and linear
- The data is ordinal and the relationship is linear
- The data is ordinal or continuous and the relationship is monotonic
The Spearman’s Rank Correlation test assumes that the variables are ordinal or continuous and that the relationship between them is monotonic. It does not require the relationship to be linear or the data to be normally distributed.
What are the limitations of using mean as a measure of central tendency?
- It can't be used with large data sets
- It can't be used with small data sets
- It is difficult to calculate
- It is highly sensitive to outliers
The main limitation of the mean as a measure of central tendency is that it is highly sensitive to outliers or extreme values. An outlier can skew the mean and make it a less accurate representation of the data. Moreover, mean does not describe the middle value or most common value in the dataset, which are often important characteristics.
What does a 95% confidence interval estimate?
- The mean of the sample
- The range within which 95% of the data points lie
- The standard deviation of the population
- The true population parameter with a 95% level of confidence
A 95% confidence interval estimates the range within which we are 95% confident that the true population parameter lies. It is not about the range of the data or the mean of the sample.
In a Chi-square test for independence, small expected frequencies can lead to a ________ Chi-square value.
- constant
- larger
- smaller
- zero
In a Chi-square test for independence, small expected frequencies can lead to a larger Chi-square value. This is because the Chi-square value is inflated by small expected frequencies, which can lead to a significant result even when there is no substantial relationship between the variables.
What is the purpose of hypothesis testing in statistics?
- To compare the sample mean to the population mean
- To make inferences about a population based on sample data
- To understand the distribution of the data
- To visualize the data
Hypothesis testing is a statistical method that is used in making statistical decisions using experimental data. It's an inferential statistic that allows us to infer if our observed results deviate from null hypothesis by chance or by a true statistical difference.
The p-value in a hypothesis test is the probability of getting a sample statistic as extreme as the test statistic, given that the _______ hypothesis is true.
- Alternative
- Null
- Original
- Random
In the context of hypothesis testing, the p-value is the probability of observing a test statistic as extreme as the one calculated, assuming that the null hypothesis is true.
What are the assumptions required for a distribution to be considered a Poisson distribution?
- The events are dependent on each other
- The events are occurring at a constant mean rate and independently of the time since the last event
- The events have more than two possible outcomes
- The number of trials is fixed
The key assumptions for a Poisson distribution are that the events are happening at a constant mean rate and independently of the time since the last event. This is often used for modeling the number of times an event occurs in a given interval of time or space.