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Statisticians in the organization in a very separate department called the quality control department would do the assessment. With the application of the sigma six mechanisms, mainly the branch called professionalizing, all the quality controls can be monitored during the production processes. In the end, this will minimize the operational cost incurred by the company.
It is because the standard error involved in survey data is inversely proportional to the sample size, and as the sample size increases, standard error decreases and, hence, the accuracy of confidence interval increases so that the sample mean approaches more close to the population mean in probability (i.e., consistency improves to great extent).
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For 80% confidence with 4 degrees of freedom, the critical value of χ2 is 7.779 and 1.064. Therefore, an 80% confidence interval for the population variance (σ2) of glove use per week is = 16.71 to 122.18. An 80% confidence interval for the population standard deviation (σ) of glove use per week is = 4.09 to 11.05.
The graph indicates that there is a relationship between GDP and Logistics Bill. The countries with lower GDP have lower logistics bills and those with higher GDP have higher logistics bills. The table below shows the regression statistics which corresponds to the 95% confidence interval for the regression coefficient.
Geography utilizes many statistical tools to make conclusions about the features of geographical data. A particular statistical technique is useful in analyzing the category of data. The independent variable is the age of the casualty while the dependent variable is deaths from the number of deaths.
It is skewed to the left. Accordingly, the weighted distance to 5 employment centers in most of the houses is less than 6. The mean of the dist variable is 3.795. The probability value is less than 0.05 of the significance level. Therefore, one rejects the null hypothesis and accept the alternative hypothesis, i.e., the price variable is normally distributed.
This kind of ANOVA has three sources of variation, namely treatment, error, and total. The treatment source measures the variations between the results of the treatment, i.e., the explained variations. The error source collectively measures the variations in scores within the respective treatments, i.e., the unexplained variations.
Many males had different height-arm spans. b) Ho: The distance from the sole of the feet to the tip of the head is the same as the distance between the outstretched hands. Ha: The distance from the sole of the feet to the tip of the head is not the same as the distance between the outstretched hands.
It is used as a forecasting model to predict future values from the previously observed values (Boashash, 2003). The forecasting methods used in time series analysis are briefed below. ARIMA (Auto-Regressive Integrated Moving Average) model is a general forecasting model that can be rationalized by transformations like differencing and logging.
These factors included text messaging, emails, MMS packs, internet, camera quality GPS accuracy, and age. Also, the study was to determine the relationship between the satisfaction level of people from rural and urban areas as well as gender concerning different factors mentioned above. Statistical methods include t-test and regression analysis.
Variability and uncertainty are concepts that have been around for several decades. For us to be able to predict future outcomes of an event or action, the two components must be determined. While variability is a physical phenomenon that can be measured, analyzed, and explained, uncertainty involves knowledge (Vose, 2002).
From the histogram, it is evidenced that following the introduction of this commodity (1-liter Orange Juice) in the Perth suburban market, it took time for the customers to familiarize themselves with the newly launched orange juice. The sales slugged in between 83rd and 100th week as evidenced by the sales of only four cartons of 1-liter orange juice.
The statistical proof of any incident involves a few steps that have to be followed sequentially with great rigor and care. Before resorting to any statistical operation it is necessary to mention and explain these steps. The process starts with a clear understanding of the concerned phenomenon; any vague idea would be catastrophic for further proceeding.
Particularly, it explores the sampling method, sample size, variables used, and data collection, from which it evaluates how applicable and relevant the authors of the articles used the techniques (Goodman, 2011). Further, the paper evaluates how the authors analyzed their data from where the discussions were based and conclusions drawn.
Quantitative analysis helps to gather and analyze measurable data from a particular focus group or subgroup with strong numerical evidence that helps to apply the results to the large-scale population using probability theories. The paper analyzes the 2002 European social surveys to find out the prevalence of racial discrimination in the UK law field.
In this case then, the probability of either A or B happening, = P (A) +P (B); =0.30+0.20; =0.50. The probability of neither A nor B happening, =P (A) +P (B)-[P (A) and P (B)]; =0.5-0.06; =0.44. P(X) + P(Y); =0.05+0.02; =0.07 is the probability of either X or Y occurring. 0.07-(0.02*0.05); 0.07-0.001; 0.069 is the probability that neither X nor Y will happening.
Each of the vehicle types has a set of unique advantages and disadvantages. The performance of hybrid vehicles is characterized by more power and covers more miles per gallon of fuel than gasoline vehicles. Additionally, the analysis of performance shows that hybrid vehicles are more favorable to the environment than gasoline vehicles.
Range of the sample = (8-2) = 6. Range of population = (6-2) = 4. The population means are less dispersed compared to the sample means. Sample mean = (0.333 + 1 + 2 + 1.333 + 2.333 + 3.333 + 3 + 3 + 2.333 + 3.333) = 20 / 10 = 2. Population mean = 10 / 5 = 2. Comparing the values of both the sample mean and the population mean, it is found out that they are equal.
Experimental errors aren't included in the data set. The probability of an outlier to occur in distribution is always there indicating that either there is a measurement error or that the population distribution is heavy-tailed. In the case of experimental errors, one may wish to do away with the statistics or utilize statistics that are strong for outliers.
Most university students prefer to have something to eat near them during school days because their bodies are actively growing and they might feel hungry over time. Most students prefer to enroll in universities where there is access to foodstuffs (Kokker 2002, p. 17). It determines the number of students to join the campus with a cafeteria (Trutna 2011, p. 29).
The study goes ahead to identify the type of food that would be required should the canteen be established. It is the role of the management to identify what should be put in the canteen depending on the survey that results in students’ requirements (Thomashow 2014). The study helps generate information that is required in making a decision.
Without considering the random variability in growth rates, extend the worksheet in Figure 16.18 to 30 years. Confirm that by using the constant annual salary growth rate and the constant annual portfolio growth rate, Tom can expect to have a 30-year portfolio of $627,937. Analyze Tom’s annual investment rate to reach a 30-year, $1,000,000 goal.
S/he comments on the summary statistics of the main features and distribution characteristics of JOBINC, and using JOBINC, s/he carries out significance tests on two and more than two population means. The graph below represents a box plot representing the variable JOBINC used to describe the overall distribution of responses for a group.
The total weighted profit of $13,845 is the highest due to action S1, it should be chosen, since EMV for International Expansion (S1) is the highest. One reformulates table 3 by subtracting the minimum element from each row. While dealing with conditional profits, a higher value for each state of nature shall be considered for calculating opportunity losses.
The value of the Pearson coefficient does not in any way generally or rather totally indicate the relationship that exists between the two variables (Mahdavi & Babak, 2012). The coefficient values range from -1 to +1, any value close to +1 means that there is a strong positive linear correlation while a value of -1 means a perfect negative linear correlation.