Comp2mm STAT Warm Up

This is an example of inferential statistics because the survey conducted by Pulse Asia is a sample of the population (2,400 Filipinos) and the results are used to make inferences or generalizations about the entire population of Filipinos. The statistic that 95% of Filipinos do not want Boy Abunda to be the secretary of the Department of Tourism is based on this sample and is used to make an inference about the opinions of all Filipinos.

Marital status, gender, and brand names are examples of qualitative variables. Qualitative variables are non-numerical variables that represent qualities or characteristics. In this case, marital status, gender, and brand names are not numerical values but rather categories or labels that describe certain attributes or characteristics of individuals or products. Therefore, they can be classified as qualitative variables.

Quantitative variables that are discrete can only take on specific values and there are typically gaps between these values. This means that the variable can only have certain defined values and cannot take on any value within a range. The term "discrete" refers to variables that are distinct and separate, rather than being continuous or having an infinite number of possible values.

A grouping of data into mutually exclusive classes showing the number of observations in each is called a frequency distribution. It is a way to organize data into categories or intervals and display the count or frequency of observations in each category. By presenting data in this format, it becomes easier to analyze and understand patterns, trends, and distributions within the data set.

The bell-shaped curve is commonly referred to as the "normal" curve. It is named so because it represents a normal distribution or a symmetrical distribution of data where the majority of values cluster around the mean, resulting in a bell-shaped pattern. This curve is widely used in statistics to analyze and understand various phenomena in fields such as social sciences, economics, and natural sciences.

A confidence interval is a statistical range of values that is used to estimate a population parameter with a specified probability. It provides a range of possible values within which the true population parameter is likely to fall. The level of confidence associated with the interval indicates the probability that the true parameter lies within the range. Therefore, a confidence interval is an interval estimate that allows for uncertainty and provides a measure of the precision of the estimate.

The specified probability that is mentioned in the question is commonly referred to as the "level of confidence." It represents the degree of certainty or reliability in the statistical analysis or estimation being conducted. The level of confidence indicates the likelihood that the true population parameter falls within a certain range or interval.

A hypothesis is a statement about a population parameter that is developed for the purpose of testing. It is a proposed explanation or prediction for a phenomenon or relationship between variables. Hypotheses are typically formulated based on prior knowledge, observations, or theories, and are then tested using empirical data through statistical analysis. The correct answer is "Hypothesis" or "hypothesis" because it accurately describes the statement about a population parameter that is developed for testing purposes.

The level of significance, also known as the significance level, is the probability of rejecting the null hypothesis when it is true. It represents the maximum allowable probability of making a Type I error, which is the incorrect rejection of a true null hypothesis. In hypothesis testing, a significance level is chosen before conducting the test, and if the p-value (probability value) of the test statistic is less than or equal to the significance level, the null hypothesis is rejected. Therefore, the level of significance plays a crucial role in determining the outcome of hypothesis testing.

If a curve is positively skewed, it means that the tail of the curve extends towards the right side of the distribution. This indicates that there are more extreme values on the right side of the curve, pulling the mean towards the right. Therefore, the statement "the curve is skewed to the right" is true.

A sample refers to a subset or portion of the population of interest that is selected for observation or study. It is used in statistics to gather data and make inferences about the larger population. By studying a sample, we can draw conclusions about the characteristics and behaviors of the entire population. Therefore, the answer choice "Sample" accurately describes a portion or part of the population of interest.

The correct answer is "median" because it refers to the middle value in a set of numbers when they are arranged in either ascending or descending order. The median is not affected by extreme values and provides a measure of central tendency that is more representative of the data set. Unlike the mean, it does not take into account the actual values of the numbers, only their position in the ordered list.

The correct answer is Variance. Variance is a measure of how spread out the data is from the mean. It calculates the average of the squared differences between each data point and the mean. By squaring the deviations, it ensures that negative and positive deviations do not cancel each other out. Variance is commonly used in statistics to understand the variability and dispersion of a dataset.

Ordinal level data can be ranked because it represents categories or variables that have a natural order or hierarchy. In the case of ITEO Faculty Evaluation, the data collected would likely involve ranking the faculty members based on their performance or other criteria. This ranking allows for a clear distinction between the different levels of performance or quality, making it suitable for ordinal data.

The sample mean refers to the average of a sample, which is calculated by summing up all the values in the sample and dividing it by the number of observations. It is a measure of central tendency that represents the typical value of the sample. The sample mean is often used to estimate the population mean, as it provides an unbiased estimate when the sample is representative of the population. Therefore, the given answer "sample mean" accurately represents the concept of the average of the sample.

A parameter is a characteristic of a population that is usually unknown and is estimated using sample data. It represents a fixed value that describes the population, such as the population mean or standard deviation. In contrast, a statistic is a characteristic of a sample and is used to estimate the corresponding parameter. The mean or average is a statistic that represents the central tendency of a sample, while the parameter would represent the true mean or average of the population.

The mode is the value that appears most frequently in a set of observations. It is a measure of central tendency that helps identify the most common or popular value in the data. Unlike the mean and median, the mode does not consider the magnitude of the values, only their frequency. Therefore, the mode is the correct answer in this case as it specifically refers to the value that appears most frequently.

The alternative hypothesis is a statement that is accepted if the sample data provides enough evidence to reject the null hypothesis. In hypothesis testing, the null hypothesis is the default assumption, and the alternative hypothesis is the opposite or alternative assumption. The alternative hypothesis is typically what the researcher is trying to prove or find evidence for. If the sample data strongly supports the alternative hypothesis, it suggests that the null hypothesis is false, and the alternative hypothesis is accepted.

The range between classes/groups is called an interval. This refers to the numerical distance or gap between different categories or groups in a dataset. It helps to organize and analyze data by dividing it into distinct intervals or ranges. By using intervals, we can understand the distribution of data more effectively and make comparisons between different groups or categories.

Nominal level data can only be classified into categories or groups without any specific order or hierarchy. This type of data is used to label or categorize variables, such as cellphone networks. It does not provide any information about the magnitude or numerical values associated with the variables. Therefore, the correct answer is "Nominal."

The correct answer is "dependent". In statistics and data analysis, the dependent variable is the variable that is being predicted or estimated based on the values of other variables, known as independent variables. The dependent variable is the outcome or response variable, while the independent variables are the predictors or explanatory variables.

The term "independent" refers to variables that are not influenced or affected by other variables in the estimation process. These variables can be manipulated or controlled by the researcher to observe their impact on the dependent variable. In statistical analysis, independent variables are used to predict or explain the changes in the dependent variable. Therefore, the correct answer is "independent" because it accurately describes variables that provide the basis for estimation without being influenced by other factors.

The term "population" refers to the entire set of individuals or objects of interest, or the measurements obtained from all individuals or objects of interest. In the context of statistics, it represents the complete group that researchers want to study and make inferences about. This can include people, animals, plants, or any other relevant entities. The population is often too large to study directly, so researchers typically select a smaller subset called a sample to gather data from.

The correct answer is "Null hypothesis." The null hypothesis is a statement about the value of a population parameter that is assumed to be true until proven otherwise. In hypothesis testing, the null hypothesis is tested against an alternative hypothesis to determine if there is enough evidence to reject the null hypothesis in favor of the alternative hypothesis. It is an essential component in statistical analysis as it helps researchers make conclusions about population parameters based on sample data.

Interval level data includes the characteristics of the ordinal level, but in addition, the difference between values is a constant size. This means that the intervals between values have a consistent and meaningful interpretation. In the case of income brackets, for example, the difference between each bracket is the same amount of income. This allows for meaningful comparisons and calculations, such as finding the average income or determining the difference between two income brackets.

Inferential statistics is the correct answer because it refers to the methods used to make inferences or draw conclusions about a population based on a sample. It involves analyzing and interpreting data from a sample in order to make generalizations or predictions about the larger population. This is different from descriptive statistics, which simply describe and summarize the data without making any inferences. Informative statistics and theoretical statistics are not commonly used terms in statistics, and sampling is a technique used to select a representative subset of a population for study.

The F-Distribution is used in ANOVA (Analysis of Variance). ANOVA is a statistical method used to compare the means of two or more groups to determine if there are any significant differences between them. The F-Distribution is used to calculate the F-statistic, which is then used to determine the p-value and make inference about the significance of the differences between the groups.

The stem-and-leaf technique is advantageous because it maintains the identity of the observation. This means that the original data values can be easily identified and retrieved from the stem-and-leaf plot. This is important for data analysis and interpretation as it allows for a more detailed understanding of the data set.

The standard error is a measure of the variability or dispersion of a sample statistic, such as the mean. It is calculated as the square root of the variance, which is a measure of how spread out the values in a dataset are. Therefore, the square root of the variance is the correct answer because it represents the standard error.

The highest and lowest value of a class or group is referred to as the class limit. Class limits define the boundaries of the class and help in organizing data into different groups or intervals. They provide a clear understanding of the range within which the values of a particular class fall. By determining the class limits, we can effectively analyze and interpret data in a structured manner.

A statistic is a numerical value that summarizes a sample or a population. It is used to make inferences or draw conclusions about the population based on the information obtained from the sample. In this context, a character of the sample refers to a specific attribute or characteristic that is being measured or observed. Therefore, a statistic is an appropriate term to describe this concept as it represents a calculated value based on the sample data.

Relative frequency shows the fraction of the total number of observations in each class. It is calculated by dividing the frequency of each class by the total number of observations. This allows for a comparison of the proportion of observations in each class, providing a relative understanding of the distribution of data.

The p-value is the probability of observing a sample value as extreme as, or more extreme than, the value observed, when the null hypothesis is true. It is used in hypothesis testing to determine the statistical significance of results. A small p-value indicates strong evidence against the null hypothesis, suggesting that the observed result is unlikely to occur by chance alone. Conversely, a large p-value suggests that the observed result is likely to occur by chance, and there is not enough evidence to reject the null hypothesis.

Descriptive statistics refers to the methods used to organize, summarize, and present data in a way that provides information and insights. It includes measures such as mean, median, mode, standard deviation, and range, which help to describe the central tendency, variability, and distribution of the data. By using descriptive statistics, researchers and analysts can effectively communicate the characteristics of a dataset and make informed decisions based on the information provided.

Continuous variables are those that can take on any value within a specific range. Unlike discrete variables, which can only take on specific values, continuous variables can be measured with great precision. Examples of continuous variables include height, weight, temperature, and time. These variables are often represented on a continuous scale, such as a number line, and can have infinite possible values between any two points.

The correct answer is "dummy" because a dummy variable is a variable that takes on only two possible outcomes, usually represented as 0 and 1. It is often used in statistical analysis and regression models to represent categorical variables. In this context, the other options (dependent, co-dependent, independent, scattered) do not specifically refer to variables with only two possible outcomes.