Core Applications of Statistical Analysis Questions
In this course, you learned core applications of statistical analysis to solve real-world personal or professional inquiry problems. You also learned different techniques to draw conclusions from data. These experiences allowed you to practice designing an approach to a statistical problem, considering assumptions and constraints, and developing interpretations and conclusions. Think about how you felt when you first started the course and how you feel now. Reflect on what you learned in this course and the applications of statistical concepts in your personal and professional life.
In your initial discussion post, specifically address the following:
- What are some practical uses in your own life for the skills you from statistic class?
- How is data analysis changing in the world around you, including workplaces?
- How can statistics be persuasive and misleading?
In your response posts to at least two peers discuss the following:
- Do you look at statistics differently now? If so, how?
- What advice can you offer to help others make the most appropriate use of data?
Explanation & Answer length: 5 Questions.
1 Predicting Sales based on Median Square Feet Stacy Blaise Southern New Hampshire University Course May 13,2021 2 Generating a Representative Sample The objective is to determine whether the median square foot or the size of the property has any impact on the selling price of the properties. For this purpose, real estate county data for the year 2019 has been selected. It includes information about the property region, county, median listing price, median per square foot and median square feet. The data contained information about different properties located in different counties. The region selected for the study is the northeast region. A random sample of 30 data is selected for the study. The data is shown below. 3 Descriptive Statistics Median listing Price Median Square Feet $192,268 1726.48 $167,058 1736.60 $83717.30 264.73 Mean Median Standard deviation The average listing price of the randomly selected sample was $192,268 (SD=83717.30). The standard deviation is relatively high, indicating variation in listing price.
Further,50% of the randomly selected data had a price higher than $!67058. The average size of the property was found to be 1726.48 square feet (SD=264.73). Also,50% of the randomly selected data had a size larger than 1736.60 square feet. Analyze Sample Comparing with Population Table Showing Summary Statistics of the Population Data Mean Sample Population Median listing Median Square Median listing Median Price Feet Price Square Feet $192,268 1726.48 $288,407 1944 4 Median Standard deviation $167,058 83717.30 1736.60 264.73 $256,936 $163,986 1901 367 From the above comparison, the mean of the median and mean listing price of the sample is less than that of the population. Further, the standard deviation was also found to be less than that of the population. Similar is the case with the area where the mean, median and standard deviation of the national market is higher than that of the randomly selected regional market. Method to get Random sample The region sample is created by first selecting all the data of the northeast regions.
Now each region was assigned a number using Excel with the formula =randbetween (1,100). It indicates assigning number randomly of each subset from 1 to 100. The number assigned was then sorted in increasing order meaning starting from 1 until the number assigned highest to the sample. After sorting based on the increasing value of random values, the first 30 samples were taken. In this way, random samples of 30 regions were obtained. Scatterplot A scatterplot of the median square feet and the median listing price is shown in the below figure. Here, the independent variable is the size of the property (median square feet) and the dependent variable is the median listing price in dollar. 5 Median listing price in $ Scatter Plot y = 238.89x - 220171 R² = 0.5707 $500,000 $450,000 $400,000 $350,000 $300,000 $250,000 $200,000 $150,000 $100,000 $50,000 $0 0 500 1000 1500 2000 2500 3000 Median Square Feet Observe Pattern The scatter plot shows median square feet or the size of the property on the x-axis and the price of the property (median listing price) on the y-axis.
The independent variable or the median square feet helps make predictions. Based on the size of the property, we can then estimate the price of the house based on the linear regression. The scatter plot shows a positive trend line indicating an increase in the size of the property will lead to an increase in the sales of the property. The scatter plot shows a positive association meaning a larger property size has a higher sales figure. The shape of the scatter plot shows a linear trend. A trend line with the equation is shown in the above figure. The linear regression can be written as; Sale=238.89Area-220171 For the house of 1200 square foot, the sales price would be $66497( 128.891200-220171).
The scatter plot has potential outlines (x=2398,y=459154). The outliers appeared as extreme observation to the right. It appeared because of the maximum size of 2398 sq foot for the randomly selected data. 6 Running Head: LA PETITE COSETTE MARKET RESEARCH MKT 113: Final Project Part I Final Submission Stacy Blaise Southern New Hampshire University 1 LA PETITE COSETTE MARKET RESEARCH 2 La petite Cosette is a new pet food line tailored to cater to the nutritional needs of both cats and dogs. La petite Cosette has a healthy, non-GMO, all-natural formulation. This research will analyze the most suitable target market and marketing strategy that would be used in the successful launch of La petite Cosette in the market. The SWOT matrix of La petite Cosette will analyze its strengths, weaknesses, opportunities, and threats (Malhotra et al., 2017). The main strength of La petite Cosette is its high-quality ingredient formulation that is guaranteed to keep pets safe, healthy, and energized. The product will incorporate a higher fiber content, improved freshness, and non-processed meat, making it superior to other natural pet food lines in the market.
Furthermore, the company has a strong brand presence, both at a retail level and online spaces. The primary weakness of La petite Cosette is its higher price point compared to other products in the market. Furthermore, the company’s low social media presence may hinder marketing efforts. The pet’s food line's main opportunity is the increased prospect in health and nutritional food markets. More dog and cat owners are becoming concerned about their health and nutritional status of their pets and are willing to spend more to maintain this factor. Furthermore, there is an upward trend in dog and cat ownership in the US, meaning more prospective customers for the product. Consequently, the chief threat of La Petite Cosette is the increased competition in the organic pet food market.
A comprehensive SWOT analysis of the new pet food line provides a basis for creating a proper marketing plan. In this case, the marketing strategy will emphasize the high nutritional content of La petite Cosette. Market segmentation will focus on identifying high-earning individuals in an urban area as its primary customers to counter its higher price point. The product takes full advantage of the growing prospect in the organic pet food market. LA PETITE COSETTE MARKET RESEARCH 3 For effective identification of the target market, demographic, psychographic, and geographic information will be implemented for the market segmentation (Martin, 2011). La petite Cosette would be marketed to single and familiar households living in the high-income neighborhood of Aventura, Miami, FL. This neighborhood primarily comprises people of Caucasian origin with a high income and high educational background.
Currently, the estimated median household income of Ventura stands at $77,597. Based on the wealth and education of the neighborhood, there is a higher likelihood of pet owners of being more willing to spend more to obtain the best quality foods for their pets. Furthermore, due to the upscale and suburban lifestyle of Aventura, residents usually spend their leisure time in outdoor activities, including golfing, shopping, and taking walks in the beaches and parks. Therefore, the product will be geared towards targeting individuals who are expected to have pet companions with them during those activities. The demographic, psychographic, and geographic segmentation of Aventura residents plays a significant role in determining their needs and wants. In relation to dog owners, the majority of highly educated, high-income earners are concerned about the quality of products for their pets, regardless of the price.
Nutritionally, pet owners of Aventura are on the market for higher protein, higher fiber food products. Furthermore, there is a particular emphasis on organic, ethically sourced materials in their pet’s food ingredients. Due to the busy lifestyle that the target market leads, their primary want is product convenience. Therefore, the users need to use and store the food product without any difficulties or time-consuming steps. Since the consumers are high profile individuals of society, they would want to obtain their foods from a highly reputable brand. Therefore, La petite Cosette is a Pet food product that will satisfy the needs and wants of Aventura residents. The high-quality ingredients will satisfy the nutritional LA PETITE COSETTE MARKET RESEARCH 4 needs of both dogs and cats.
Furthermore, its non-GMO and all-natural factor addresses the customers’ organic wants for their pets. Additionally, La Petite Cosette is a pet line associated with a reputable brand. Social media is the primary marketing channel for La Petite Cosette. Specifically, social networks and the company’s website will be great avenues to advertise the product. Due to their high income and higher education, the residents of Aventura will have a high propensity towards social media. Therefore, the main aim would be to increase the social media presence of the products in highly influential social networks. Facebook, Instagram, Tik Tok would be most suitable to advertise the product. Advertisements through these platforms, using the right hashtags, will ensure that the target market receives the message.
La petite Cosette would also benefit from a highly functional company website with high-ranking pages. SEO strategies, including keyword ranking, backlinking, and creating attractive landing pages, will increase consumer awareness of the product (Tuten, 2020). To effectively reach the targeted markets, highly specific keywords and hashtags for Aventura residents will be used. Social media marketing has been used by companies such as Nike for its online presence, to grow its brand authenticity, and engage in public relations. In terms of the 4P’s, La Petite Cosette is a highquality product that offers a nutritional and environmental advantage. Therefore, due to the versatility of social media platforms, they can communicate the qualities of the products effectively. In relation to price and promotion, social media provides the advantage of customizing both the price and the target markets of the product. Therefore, marketing efforts would be geared towards directing advertisements towards high-income individuals, with or without families.
Lastly, social media offers the advantage of the geographical targeting of LA PETITE COSETTE MARKET RESEARCH 5 customers. Therefore, advertisements on all platforms would be directed towards the Aventura neighborhood. The main marketing strategy that would not be recommended for the launch of La petite Cosette is through public relations. This includes activities such as press releases, media interviews, sponsorships, conferences, and host events.
These strategies would ineffective in the promotion of La Petite Cosette since they focus on promoting the company, and not the specific product. For example, press releases are not conducive for emphasizing the specific high-quality features of La Petite Cosette. In relation to the 4P’s, the product the new La petite Cosette. The promotion strategies will include press releases, conferences, sponsorships, and hosting events. The geographical location of public relations would be Aventura, Miami. That said, the promotion factor of public relations would be changed, then the strategy would be more likely to succeed. In conclusion, La petite Cosette is a new pet food line with high nutritional, organic, all-natural formulation, specifically for cats and dogs. The main strength of the product is its high ingredient formulation, while its main weakness is the higher price point as compared to other products.
Market segmentation involves targeting family and individual pet owners in the high-income neighborhood of Aventura ,Miami. Due to the high-income level and educational background of the target market, there is an emphasis on the quality of products over its price. Therefore, the La Petite Cosette would be accepted easily in the market. The most effective marketing strategy for the product line is social media platforms. LA PETITE COSETTE MARKET RESEARCH 6 References Malhotra, N. K., Nunan, D., & Birks, D. F. (2017). Marketing research: An applied approach. Pearson Education Limited. Martin, G. (2011). The importance of marketing segmentation. American Journal of Business Education (AJBE), 4(6), 15-18. Tuten, T. L. (2020). Social media marketing. Sage. Regional vs. National Housing Price Comparison Report Report: Regional vs. National Housing Price Comparison Stacy Blaise Southern New Hampshire University 1 Regional vs. National Housing Price Comparison Report 2 Introduction Purpose: 2 hypothesis tests and 1 confidence interval interpretation are needed to be done to determine that region’s housing prices and square footage of homes are different from national values.
Sample: 2 samples are drawn randomly from pacific region data set. 1st sample is 100 housing prices and the 2nd is 100 square footage data. Questions and type of test: One sample t test is done to test the claim that average regional housing price is greater than the national housing price. It’s a right tailed test. One sample t test is also done for the second test, to test the claim that average regional square footage of home is different from the national square footage of homes.
That’s a two tailed test. 1-Tail Test Hypothesis Population parameter: National housing price Null hypothesis: Average regional housing price is same as the national housing price Alternative hypothesis: Average regional housing price is greater than the national housing price H0 : μ = 288407 H1 : μ > 288407 Regional vs. National Housing Price Comparison Report 3 Data analysis Histogram Housing price 40 35 Frequency 30 25 20 15 Frequency 10 5 0 Bin Summary statistics Mean Standard Error Median Mode Standard Deviation Sample Variance Kurtosis Skewness Range Minimum Maximum Sum Count 399310.8 15747.92 349950 450050 157479.2 2.48E+10 2.347826 1.467454 799475 198050 997525 39931077 100 Regional vs. National Housing Price Comparison Report 4 Quartiles Q1 Q3 295412.5 450050 Summary of sample data Center of the data set is measured by the median, it is 349950.
Data spreading is the standard deviation which is equal to 157479.2. We have a right skewed shape for housing price and can be assumed to be normally distributed since sampling is also done as simple random sampling. Sample size is also saying about the normally as it is greater than 30. Hypothesis Test Calculations: Test statistics ?= t= ?̅ − ? ?/√? 399310.8 − 288407 157479.2/ √100 t = 7.042 P value P value is calculated using excel function =T.DIST.RT([test statistic], [degree of freedom]) P value = 0 Interpretation: Calculated p value is less than the significance level 0.05.
Null hypothesis is rejected We can conclude that regional housing price is greater than the national housing price Regional vs. National Housing Price Comparison Report 5 2-Tail Test Hypothesis Population parameter: National square footage of homes Null hypothesis: Average regional square footage of home is same as the national square footage of homes Alternative hypothesis: Average regional square footage of home is different from the national square footage of homes H0 : μ = 1944 H1 : μ ≠ 1944 Data Analysis: Histogram 20 18 16 14 12 10 8 6 4 2 0 Frequency 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300 2400 2500 2600 More Frequency Square foot Bin Regional vs. National Housing Price Comparison Report 6 Summary statistics Mean Standard Error Median Mode Standard Deviation Sample Variance Kurtosis Skewness Range Minimum Maximum Sum Count 1904.036429 27.31287476 1828 2588 273.1287476 74599.31277 0.330794159 0.322346174 1324 1264 2588 190403.6429 100 Quartiles Q1 Q2 1765.125 2079.875 Summary of sample data Center of the data set is measured by the median 1828.
Spread of data is measured by the standard deviation which is equal to 273.13. Square footage data is approximately symmetric according to the histogram and can be assumed to be normally distributed since sampling is also done as simple random sampling with sample size which is greater than 30. Hypothesis Test Calculations: Test statistics ?= t= ?̅ − ? (?/√?) 1904.04 − 1944 273.13/ √100 Regional vs. National Housing Price Comparison Report t = −1.463 P value P value is calculated using excel function =T.DIST.RT([test statistic], [degree of freedom]) P value = 0.147 Interpretation: Calculated p value is greater than the significance level 0.05.
Null hypothesis is not rejected We do not have sufficient evidence to support the claim that average regional square footage of home is different from the national square footage of homes. Comparison of the Test Results: Confidence interval equation ?? = ?̅ ± ? ? ( ? ? √? ) tα/2 = t0.025,99 = 1.984 Substitute values CI = 1904.04 ± 1.984 273.13 √100 CI = 1904.04 ± 54.19 ?? = (????. ??, ????. ??) 95% confidence interval of average square footage is (????. ??, ????. ??) 7 Regional vs. National Housing Price Comparison Report 8 Interpretation We are 95% confident that true mean of square footage of homes lies between 1849.85 and 1958.23. Since the national average square footage 1944 lies between the calculated confidence interval, null hypothesis is not rejected. Final Conclusions Summary Two samples are created from pacific region population data. They are housing price sample and square footage of homes.
Tested the claim that Pacific regional average housing price is higher that, that of for population. We concluded that the claim is accepted since the null hypothesis is rejected according to test results. Then one sample t test was done to test the claim that average regional square footage of home is different from the national square footage of homes. The conclusion is average regional square footage of home is not different from the national square footage of homes. This is confirmed by confidence interval interpretation.
As per the results of 2 hypothesis tests and 1 confidence interval interpretation, drawn conclusions are acceptable. Hypothesis Testing for Regional Real Estate Company HypothesisTesting for Regional Real Estate Company Stacy Blaise Southern New Hampshire University 1 Hypothesis Testing for Regional Real Estate Company 2 Introduction This real state data analysis is done to test the pacific region salesperson’s claim that the average cost per square foot of his home sales is above the average cost per square foot in the Pacific region. Sample of 1001 cost per square foot in the Pacific region is given for the analysis. As per the claim, left tailed t test is done using the 0.05 significance level.
Setup Population parameter Mean of cost per square foot Null hypothesis H0 : Average cost per square foot in the pacific region is 275 Alternative hypothesis H1 : Average cost per square foot in the pacific region is less than 275 This is a left tailed test Significance level: α = 0.05 Data Analysis Preparations Sample is the home sales of the Pacific region, sample size is 1001. [Provide the descriptive statistics of the sample.] Mean Standard Error Median Mode Standard Deviation Sample Variance Kurtosis Skewness 264.016393 5.11263417 202.965842 206.165334 161.756506 26165.1672 4.50212972 2.08640264 Hypothesis Testing for Regional Real Estate Company Range Minimum Maximum Sum Count 3 967.451596 103.832378 1071.28397 264280.409 1001 Histogram 600 Frequency 500 400 300 Frequency 200 100 0 200 300 400 500 600 700 800 900 1000 More Bin Histogram shows that the data set is right skewed. Median of the data set is 203 while the mean is 264.02. Da…
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