# Understanding Statistics - Essay Example

Summary
Scales of Measurement Scales of measurement determines the criteria for classifying various variables. The scales of measurements are more applicable in the academic and research scenarios as compared to the ordinary life situations…

## Extract of sample "Understanding Statistics"

The ordinal scales provide no evident variation amongst the variables. This scale only evaluates the order of the values. The ordinal scales measure the concepts that are not numeric such as fulfillment, jovialness and discomfort among others. In any analysis, an individual can elucidate that number four is better than number three though the extent is not clear again, it is not easy to determine the variation between ok and happy. The interval scales encompass numeric scales that besides providing the order, they also provide the accurate differences between the variables in question. A standard example is the Celsius temperature such as the disparity between 30 and 40 degrees is 10 degrees. Time can also provide precise variations where the disparity between six and four minutes is two minutes. Ratio scales are essential in statistical evaluations to its flexibility for alterations seeking accuracy. The ratio scale can be multiplied, added, divided or subtracted and the central tendency measures estimated. A discrete variable involves well determined set of predetermined set of probable values-states. The variables include the provision that is either “true” or ”false”, the team that will win and the number of dime in a pouch amongst others. Nonetheless, the variables might appear to be discrete at one point and continuous at a different perspective. The continuous variable opts to take on a position between two extreme positions or values. Continuous variables encompass the indoor temperature, direction travelled or the water used. The discrete variable tends to depict a digital quantity whilst the continuous variable tends to be analog in quantity. According to the explanations provided on the above scales, the different statistical research studies can select any of the according to suitability for application. The continuous and the discrete variables have significance on the selection of the research methodologies to use since the experimental method would be appropriate with discrete data whilst the researches dealing with conventional aspects might find it appropriate to employ continuous variables in the research activity. The area under the normal distribution is proportional to the overall area. The total area covered in the normal curve is equivalent to one. The curves never attain the situation Y = 0 but move to the positive infinity and the negative infinity. The shape assumed by the normal curve is infinite and depends on the mean and the standard deviation. The z-score is a critical tool in data evaluation and is used to determine the extent to which a point x is high or below the population mean ?. It is the providence of the T-statistic with predetermined mean and the standard deviation. In case of interference with the degree of freedom to an extent of assuming population mean and the standard deviation from the availed sample, then it fails to be T-statistic. The percentile rank of a normally distributed population can be estimated readily through the use of z- scores. In the e vent that the area under a curve is apportioned above and below the mean, the partitions obtained are the similar to the probability picking a value in the similar range. For instance, the area between the standard deviation above and below t ...Download file to see next pagesRead More
Cite this document
• APA
• MLA
• CHICAGO
(“Understanding Statistics Essay Example | Topics and Well Written Essays - 1500 words”, n.d.)
Understanding Statistics Essay Example | Topics and Well Written Essays - 1500 words. Retrieved from https://studentshare.org/statistics/1498259-understanding-statistics
(Understanding Statistics Essay Example | Topics and Well Written Essays - 1500 Words)
Understanding Statistics Essay Example | Topics and Well Written Essays - 1500 Words. https://studentshare.org/statistics/1498259-understanding-statistics.
“Understanding Statistics Essay Example | Topics and Well Written Essays - 1500 Words”, n.d. https://studentshare.org/statistics/1498259-understanding-statistics.
Click to create a comment or rate a document
nl
Student rated this paper as
The topic of "Understanding Statistics" was tough to find. I spent too much time to find it. Here at StudentShare, I got the most decent example. Many thanks!

## CHECK THESE SAMPLES OF Understanding Statistics

### Statistics

...Due Statistics Report II This study analyzed the number of phone calls made home by a small sample (n=40) of undergraduate students. In this study, the number of phone calls made home was the dependent variable, and both the year of the student (freshmen, sophomore, junior, or senior) and the gender of the student were independent variables. Because this study has two independent variables and one dependent variable, a factorial ANOVA design is most appropriate. The gender factor has two levels: male and female, and the year factor has four levels (freshmen, sophomore, junior, or senior), meaning we have a 4x2 ANOVA design. Because we have a 4x2 ANOVA design, one interaction effect is possible (between the gender and...
2 Pages(500 words)Research Paper

### Statistics

... the frequency distribution table: Age Frequency 16-27 26 28-39 9 40-51 5 52-63 1 64-76 9 From this frequency table, we can see that the data are skewed to the right. If we were to graph this table, we would understand that the mass of distribution is concentrated more on the left-hand side. This is because there are relatively few high values, thus skewing the graph to the right. Because this graph would not contain a normal curve, we could predict a high number of people between the ages of 16 – 27 (26 out of a total of 50 — 26/50 = 0.52 or more than half).... there are 50 numbers, we can divide these numbers into groups of 10 (5 groups). Next we subtract the lowest number from the highest number (76 — 16 = 60). After this, we will divide...
1 Pages(250 words)Math Problem

### Statistics

..., as indicated in the questionnaires as possible responses, are estimated to identify whether they make sense conceptually. (Loevinger, 1957) This ensures that the statistical model adopted has a perfect fit to the parameters of the study. The use of Chi-square statistic, the Goodness of Fit Index, the Comparative Fit Index, the Adjusted Goodness of Fit Index , use of multiple regression and finally, the root mean square used in approximating errors, serve to give a clear understanding of the research analysis model adopted in the study. Although the use of the Chi-square statistic is preferred, the sensitivity of the statistic especially in regard to...
4 Pages(1000 words)Essay

### Statistics

...1. (a) (i) Stem-and-leaf display: Stem Leaves 2 5 6 7 8 8 3 2 3 5 7 8 9 4 3 7 7 5 8 6 7 9 6 Or Stem Leaves 2 0 4 5 7 8 8 3 2 3 4 6 8 9 4 2 6 7 5 0 8 6 6 9 5 (ii) As can be seen from the diagram, most of the prices are concentrated in the 2 and 3 stems, with decreasing number of leaves in 4, 5 stems and only one leaf at 6 and 9 stems. The shape of the distribution is close to binomial distribution. (iii) The revised mean is(409*20-958)/19=380 (iv) Median for 20 house prices is (366+384)/2=377. The revised median for 19 houses is 366 (v) Q1=(283+285)/2=284 Q3=(467+472)/2=469.5 IQR=185.5 It would be better to measure spread with IQR instead of standard deviation because, unlike the standard deviation, it is not affected by values... (a) (i)...
4 Pages(1000 words)Essay

### Statistics

...Be sure to show your work in case partial credit is awarded. To receive full credit, work must be shown if applicable. Section 5 Introduction to Normal Distribution and the Standard Normal Distribution 1. Use the Standard Normal Distribution table to find the indicated area under the standard normal curve. (1 points per each part) a. Between z = 0 and z = 1.24 Area to the left of z = 0 is P(z < 0) = 0.5 Area to the left of z = 1.24 is P(z < 1.24) = 0.8925 Therefore, P(0 < z < 1.24) = P(z < 1.24) – P(z < 0) = 0.8925 – 0.5 = 0.3925 b. To the left of z = 1.68 Area to the left of z = 1.68 is P(z < 0) = 0.9535 c. Between z = -1.52 and z = -0.64 Area to the left of z = -1.52 is P(z < -1.52) = 0.0643 Area to the left of z = -0.64 is P...
2 Pages(500 words)Statistics Project

### Descriptive statistics/inferential statistics

...Survey Research Select a quantitative article that is related to your research question in which the results of some type of a survey are reported. Examine the demographic statistics and the means and standard deviations for items on the survey. What do they tell you about the sample? Discuss the ethical issues related to gathering demographic and survey data.  The study chosen was the Williams et. al. (1998) study wherein the researchers attempted to evaluate the extent to which patient literacy was related to their knowledge of asthma and effective use of the MDI. The study presents demographic statistics predominantly in the form of frequencies and percentages, and some in the form of means and S.D.’s. The data shows us... ? Read the...
2 Pages(500 words)Assignment

### Statistics

..., particularly in the U.S. This research paper therefore aims at establishing the relationships between the changes in the GDP and house sales. In determining such relationships, this research paper will focus on literature material and the statistical inferences. Resources The link between GDP and the change in the sales of the housing and real estate units is evidenced by the data on the performance if these two variable over time. The data from the department of labor and economics show that these two variables have a strong positive relationship. This is evidenced by the great global economic and financial crisis of 2009 that negatively affected the housing and real estate sector through the housing bubbles. These...
2 Pages(500 words)Research Paper

### Statistics

...Statistics Introduction Carbon dioxide’s concentration in the atmosphere is changing from time to time across the years. There are a number of evidences which show that the concentration of atmospheric carbon dioxide has been steady increasing globally. This rise is attributed to increasing human and industrial activities across the globe. Unlike moisture in the atmosphere, carbon dioxide is mixed well in the atmosphere thus its circle can go unnoticed due to relatively slow changes. The level or rather the amount of carbon dioxide in the atmosphere is measured easily because it is a greenhouse gas; causing surface warming. Therefore, this paper presents carbon dioxide data and a presentation of its trend over the...
4 Pages(1000 words)Essay

### Statistics

...due Statistics Assignment Question Summary Statistics Life Expectancy Mean 76.0752 Standard Error 0.257757 Median 76.38245 Mode #N/A Standard Deviation 1.840753 Sample Variance 3.388371 Kurtosis -0.55827 Skewness -0.66657 Range 6.808189 Minimum 71.86361 Maximum 78.6718 Sum 3879.835 Count 51 From the summary statistics, the data is normally distributed with the mean approximately equal the median. (76.0752 ≈ 76.38245). Moreover, the skewness is not statistically different from zero. Question 2 Life expected can be termed as directly influenced by the average income. The scatter diagram shows that higher income earners have higher life expectancy. Note also that the...
2 Pages(500 words)Assignment

### Statistics

...a regression model as shown below: Regression Statistics Multiple R 0.722330899 R Square 0.521761927 Adjusted R Square 0.515577814 Standard Error 635277.1578 Observations 236   Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Intercept -591420.7785 153485.3741 -3.85327 0.00015 -893824 -289017 sq-ft 326.5526297 43.67605433 7.476697 0.00000 240.5002 412.605 BATHS 160839.1163 43292.87465 3.715141 0.00025 75541.68 246136.6 BEDS 8436.754376 42477.8691 0.198615 0.84274 -75254.9 92128.43   Df SS MS F Significance F Regression 3 1.02151E+14 3.41E+13 84.37134 0.0000 Residual 232 9.36299E+13 4.04E+11 Total 235 1.95781E+14       The above regression model can be summarized as: House Price = -591420.7785 + 326.5526297...
3 Pages(750 words)Essay