Introduction To Statistics Quiz Test!

The science of statistics includes organizing data, presenting data, and interpreting data. Organizing data involves collecting and arranging data in a systematic manner. Presenting data involves representing data in various forms such as tables, charts, or graphs. Interpreting data involves analyzing and drawing conclusions from the data. Therefore, all of the given options are correct and are included in the science of statistics.

The main objective of descriptive statistics is to describe the data we collected. Descriptive statistics involves organizing, summarizing, and presenting data in a meaningful way, such as through measures of central tendency (like computing an average) and measures of variability. This helps us to understand and interpret the data, identify patterns, and make conclusions about the sample or population from which the data was collected.

A sample is a subset of a population, meaning it represents a smaller portion of the whole group being studied. It is not necessary for a sample to have a specific minimum number of observations, as the size can vary depending on the research objectives. Descriptive statistics, on the other hand, refer to the analysis and interpretation of data collected from a sample or population. Therefore, the statement "It is a part of population" is true, making the answer "All of the above are correct."

The correct answer is "It is a collection individuals, objects, or measurements." This statement accurately describes a population as a group that can consist of individuals, objects, or measurements. It is not necessary for the population to be a large number of values, and it does not exclusively refer to people. Therefore, the correct answer is that a population is a collection of individuals, objects, or measurements.

A qualitative variable is a type of variable that describes qualities or characteristics rather than quantities or numerical values. It represents non-numeric data such as categories, labels, or attributes. Therefore, the statement "Is not numeric" accurately describes a qualitative variable as it emphasizes that it does not involve numerical values or measurements.

The ordinal scale of measurement is based on ranks, meaning that data is ordered or ranked in a particular order. It does not involve any specific numerical values or intervals between the ranks. It does not have a meaningful zero point and cannot assume negative values. Therefore, the correct answer is that the ordinal scale of measurement is based on ranks.

The ratio scale of measurement is a type of measurement scale that has a meaningful zero point. This means that the zero point on the scale represents the absence of the measured attribute. Additionally, the ratio scale allows for meaningful comparisons of the magnitude of the measured attribute between different values on the scale. This scale is often used in fields where precise measurement and comparison are required, such as in scientific research or in physical measurements.

All of the given examples are continuous variables because they can take on any value within a certain range. Birth weight of babies can vary from very low to very high values, the distance between tollbooths on the New Jersey Turnpike can be any positive value, and the age of CEOs of the Fortune 500 companies can range from young to old. Therefore, all of these examples represent continuous variables.

A discrete variable is a type of variable that can only take on whole number values. Unlike continuous variables, which can take on any value within a range, discrete variables are limited to specific, separate values. This means that they cannot have fractions or decimals, and they cannot be negative. Therefore, the correct answer is that a discrete variable can assume only whole number values.

A nominal scale variable is usually the result of counting something because it represents categories or labels that do not have a specific numerical value. Nominal variables are used to classify or categorize data, such as gender (male or female) or colors (red, blue, green). Counting is a common method to determine the frequency or occurrence of different categories within a nominal variable. The other options in the question, such as having a meaningful zero point, assuming negative values, or having more than two categories, do not align with the characteristics of a nominal scale variable.