boitshupoO
2021-03-07
Answered

What is the difference between quantitative and qualitative data.

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saiyansruleA

Answered 2021-03-08
Author has **110** answers

Step 1

Data are characteristics or information, usually numerical, that are collected through observation. Data is measured, collected and reported, and analyzed, whereupon it can be visualized using graphs, images or other analysis tools.

Data is a collection of facts, such as numbers, words, measurements, observations or just descriptions of things.

Step 2

Quantitative data

-Quantitative data describes quantities or numbers, this data is in the form of numbers that can be examined through various tests, formulas, statistical software, and can be compared on a numeric scale.

-Quantitative data is objective, to-the-point, and conclusive.

-Quantitative data is considered as structured data. This type of data is formatted in a way so it can be quickly organized and searchable within relational databases.

-For example: height of an individual, weight of an individual, length of the tree stem, number of teeth, amount of enzyme produced by an organ, number of eggs laid by a hen, etc.

Qualitative data

-Qualitative data describes qualities or characteristics and may be in the form of descriptive words that can be examined by patterns, frequency or meaning.

-Qualitative data is subjective, interpretive, and exploratory.

-Qualitative data is considered unstructured or semi structured. This type of data is loosely formatted with very little structure. Because of this, qualitative data cannot be collected and analyzed using conventional methods.

-For example: color of eye, skin color, hair color, weather condition, mood of an individual, taste of food, etc.

Data are characteristics or information, usually numerical, that are collected through observation. Data is measured, collected and reported, and analyzed, whereupon it can be visualized using graphs, images or other analysis tools.

Data is a collection of facts, such as numbers, words, measurements, observations or just descriptions of things.

Step 2

Quantitative data

-Quantitative data describes quantities or numbers, this data is in the form of numbers that can be examined through various tests, formulas, statistical software, and can be compared on a numeric scale.

-Quantitative data is objective, to-the-point, and conclusive.

-Quantitative data is considered as structured data. This type of data is formatted in a way so it can be quickly organized and searchable within relational databases.

-For example: height of an individual, weight of an individual, length of the tree stem, number of teeth, amount of enzyme produced by an organ, number of eggs laid by a hen, etc.

Qualitative data

-Qualitative data describes qualities or characteristics and may be in the form of descriptive words that can be examined by patterns, frequency or meaning.

-Qualitative data is subjective, interpretive, and exploratory.

-Qualitative data is considered unstructured or semi structured. This type of data is loosely formatted with very little structure. Because of this, qualitative data cannot be collected and analyzed using conventional methods.

-For example: color of eye, skin color, hair color, weather condition, mood of an individual, taste of food, etc.

asked 2020-12-24

True or False

1.The goal of descriptive statistics is to simplify, summarize, and organize data.

2.A summary value, usually numerical, that describes a sample is called a parameter.

3.A researcher records the average age for a group of 25 preschool children selected to participate in a research study. The average age is an example of a statistic.

4.The median is the most commonly used measure of central tendency.

5.The mode is the best way to measure central tendency for data from a nominal scale of measurement.

6.A distribution of scores and a mean of 55 and a standard deviation of 4. The variance for this distribution is 16.

7.In a distribution with a mean of M = 36 and a standard deviation of SD = 8, a score of 40 would be considered an extreme value.

8.In a distribution with a mean of M = 76 and a standard deviation of SD = 7, a score of 91 would be considered an extreme value.

9.A negative correlation means that as the X values decrease, the Y values also tend to decrease.

10.The goal of a hypothesis test is to demonstrate that the patterns observed in the sample data represent real patterns in the population and are not simply due to chance or sampling error.

1.The goal of descriptive statistics is to simplify, summarize, and organize data.

2.A summary value, usually numerical, that describes a sample is called a parameter.

3.A researcher records the average age for a group of 25 preschool children selected to participate in a research study. The average age is an example of a statistic.

4.The median is the most commonly used measure of central tendency.

5.The mode is the best way to measure central tendency for data from a nominal scale of measurement.

6.A distribution of scores and a mean of 55 and a standard deviation of 4. The variance for this distribution is 16.

7.In a distribution with a mean of M = 36 and a standard deviation of SD = 8, a score of 40 would be considered an extreme value.

8.In a distribution with a mean of M = 76 and a standard deviation of SD = 7, a score of 91 would be considered an extreme value.

9.A negative correlation means that as the X values decrease, the Y values also tend to decrease.

10.The goal of a hypothesis test is to demonstrate that the patterns observed in the sample data represent real patterns in the population and are not simply due to chance or sampling error.

asked 2020-12-28

Is statistical inference intuitive to babies? In other words, are babies able to generalize from sample to population? In this study,1 8-month-old infants watched someone draw a sample of five balls from an opaque box. Each sample consisted of four balls of one color (red or white) and one ball of the other color. After observing the sample, the side of the box was lifted so the infants could see all of the balls inside (the population). Some boxes had an “expected” population, with balls in the same color proportions as the sample, while other boxes had an “unexpected” population, with balls in the opposite color proportion from the sample. Babies looked at the unexpected populations for an average of 9.9 seconds (\(sd = 4.5\) seconds) and the expected populations for an average of 7.5 seconds (\(sd = 4.2\) seconds). The sample size in each group was 20, and you may assume the data in each group are reasonably normally distributed. Is this convincing evidence that babies look longer at the unexpected population, suggesting that they make inferences about the population from the sample? Let group 1 and group 2 be the time spent looking at the unexpected and expected populations, respectively. A) Calculate the relevant sample statistic. Enter the exact answer. Sample statistic: _____

B) Calculate the t-statistic. Round your answer to two decimal places. t-statistic = ___________

C) Find the p-value. Round your answer to three decimal places. p-value =__________

asked 2022-06-26

In statistics, why do you reject the null hypothesis when the p-value is less than the alpha value (the level of significance)

This is a question that I've always wondered in statistics, but never had the guts to ask the professor. The professor would say that if the p-value is less than or equal to the level of significance (denoted by alpha) we reject the null hypothesis because the test statistic falls in the rejection region. When I first learned this, I did not understand why were comparing the p values to the alpha values. After all, the alpha values were brought in arbitrarily. What is the reason for comparing them to the alpha values and where do the alpha values of 0.05 and 0.10 come from? Why does the statement ${p}_{\text{value}}\le \alpha $ allow you to reject ${H}_{0}$?

This is a question that I've always wondered in statistics, but never had the guts to ask the professor. The professor would say that if the p-value is less than or equal to the level of significance (denoted by alpha) we reject the null hypothesis because the test statistic falls in the rejection region. When I first learned this, I did not understand why were comparing the p values to the alpha values. After all, the alpha values were brought in arbitrarily. What is the reason for comparing them to the alpha values and where do the alpha values of 0.05 and 0.10 come from? Why does the statement ${p}_{\text{value}}\le \alpha $ allow you to reject ${H}_{0}$?

asked 2020-11-12

What is a data model, and why is its purpose?
What is conceptual modeling? What do we need to use conceptual modeling?
What are the functions of a DBMS? Provide an example of their use.

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What is an interquartile range?

asked 2021-06-03

The American Journal of Political Science (Apr. 2014) published a study on a woman's impact in mixed-gender deliberating groups. The researchers randomly assigned subjects to one of several 5-member decision-making groups. The groups' gender composition varied as follows: 0 females, 1 female, 2 females, 3 females, 4 females, or 5 females. Each group was the n randomly assigned to utilize one of two types of decision rules: unanimous or majority rule. Ten groups were created for each of the

asked 2022-06-14

Why do wave packets spread out over time?

Why do wave functions spread out over time? Where in the math does quantum mechanics state this? As far as I've seen, the waves are not required to spread, and what does this mean if they do?

Why do wave functions spread out over time? Where in the math does quantum mechanics state this? As far as I've seen, the waves are not required to spread, and what does this mean if they do?