When to use the mean
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The mean should be used to summarize data that is either scaled with equal intervals like speed, weight, height, and temperature, or when the distribution is normal. The mean is sensitive to outliers in skewed distributions, so it should only be used when the distribution is close to normal.
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When to use the mean (2)
The MEAN should only be used to summarize data that is both scaled and normally distributed. Scaled data has equal intervals like speed, weight, height, and temperature. The mean is sensitive to outliers in skewed distributions so it should only be used when the distribution is close to normal.
When to use the inter quartile range
This document discusses when to use the Mann-Whitney U test. The Mann-Whitney U test can be used if the data is ordinal or if the distribution is skewed or non-normal. It is used when either one of two conditions are met: the data is ordinal or the distribution is skewed or non-normal.
When to use the median
The median is used to summarize data when either the data is ordinal or the distribution is skewed or non-normal. The median is appropriate when one of two conditions are met: if the data is ordinal or if the distribution is skewed or non-normal. The document discusses when to use the median to summarize data.
When to use standard deviation
The standard deviation, like the mean, is used to analyze scaled, normally distributed data such as speed, weight, height, and temperature. It measures how spread out numbers are from the average (mean) value. The mean should only be used when the data follows a more or less normal distribution, as outliers in skewed distributions overly influence the mean.
Presentation1
Repeatability refers to the variation in measurements taken by a single instrument on the same item under the same conditions. Less than perfect repeatability causes test-retest variability due to factors like intra-individual and intra-observer variability. To measure repeatability, multiple readings should be taken in the same place and calculated to find the average range to avoid errors. Calculating the mean, variance, and standard deviation of data readings can help eliminate repeatability errors by identifying measurement variability. Ensuring good repeatability through taking multiple readings is important for obtaining precise values and avoiding potential problems in measurement work.
Quick reminder single or multiple regression
Single-Linear Regression uses one independent variable to predict a dependent variable, such as average daily temperature predicting ice cream sales. Multiple-Linear Regression uses two or more independent variables to predict a dependent variable, for example average daily temperature and average number of community events predicting ice cream sales.
Quick reminder nature of non-parametric relationship data
This document provides guidance on which statistical tests to use when analyzing different variable types. It recommends using the phi coefficient for dichotomous variables, point-biserial for a dichotomous and scaled variable, Spearman's rho for ordinal or scaled variables, Kendall's tau for ordinal or scaled variables with ties, and Spearman's rho or Kendall's tau for two scaled variables, depending on whether there are ties.
Parametric or non parametric relationship practice problems
The director of a health clinic asked to determine the relationship between patients' age and blood pressure. Problem 1 used a parametric test since both variables were scaled and normally distributed. Problem 2 used a non-parametric test because while blood pressure was scaled and normal, gender was nominal. Problem 3 also used a non-parametric test since blood pressure was skewed, even though age was normal. Problem 4 used a parametric test because one variable was ranked. Problem 5 used a non-parametric test as both variables were nominal.
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When to use the mean (2)
The MEAN should only be used to summarize data that is both scaled and normally distributed. Scaled data has equal intervals like speed, weight, height, and temperature. The mean is sensitive to outliers in skewed distributions so it should only be used when the distribution is close to normal.
When to use the inter quartile range
This document discusses when to use the Mann-Whitney U test. The Mann-Whitney U test can be used if the data is ordinal or if the distribution is skewed or non-normal. It is used when either one of two conditions are met: the data is ordinal or the distribution is skewed or non-normal.
When to use the median
The median is used to summarize data when either the data is ordinal or the distribution is skewed or non-normal. The median is appropriate when one of two conditions are met: if the data is ordinal or if the distribution is skewed or non-normal. The document discusses when to use the median to summarize data.
When to use standard deviation
The standard deviation, like the mean, is used to analyze scaled, normally distributed data such as speed, weight, height, and temperature. It measures how spread out numbers are from the average (mean) value. The mean should only be used when the data follows a more or less normal distribution, as outliers in skewed distributions overly influence the mean.
Presentation1
Repeatability refers to the variation in measurements taken by a single instrument on the same item under the same conditions. Less than perfect repeatability causes test-retest variability due to factors like intra-individual and intra-observer variability. To measure repeatability, multiple readings should be taken in the same place and calculated to find the average range to avoid errors. Calculating the mean, variance, and standard deviation of data readings can help eliminate repeatability errors by identifying measurement variability. Ensuring good repeatability through taking multiple readings is important for obtaining precise values and avoiding potential problems in measurement work.
Quick reminder single or multiple regression
Single-Linear Regression uses one independent variable to predict a dependent variable, such as average daily temperature predicting ice cream sales. Multiple-Linear Regression uses two or more independent variables to predict a dependent variable, for example average daily temperature and average number of community events predicting ice cream sales.
Quick reminder nature of non-parametric relationship data
This document provides guidance on which statistical tests to use when analyzing different variable types. It recommends using the phi coefficient for dichotomous variables, point-biserial for a dichotomous and scaled variable, Spearman's rho for ordinal or scaled variables, Kendall's tau for ordinal or scaled variables with ties, and Spearman's rho or Kendall's tau for two scaled variables, depending on whether there are ties.
Parametric or non parametric relationship practice problems
The director of a health clinic asked to determine the relationship between patients' age and blood pressure. Problem 1 used a parametric test since both variables were scaled and normally distributed. Problem 2 used a non-parametric test because while blood pressure was scaled and normal, gender was nominal. Problem 3 also used a non-parametric test since blood pressure was skewed, even though age was normal. Problem 4 used a parametric test because one variable was ranked. Problem 5 used a non-parametric test as both variables were nominal.
Quick reminder sample size
The document discusses sample size and recommends selecting the "sample sizes equal to or greater than 30" option if the sample size of both groups in a problem is equal to or greater than 30, otherwise selecting the "sample sizes less than 30" option. It notes that this recommendation provides the foundation for all inferential statistics and directs the reader to additional resources for a more thorough explanation of why sample size is important.
Quick tip sample size
If at least one sample size is greater than 30, use a parametric test even if the distribution is skewed, as probability density, normal distributions, sampling distributions, and standard error concepts allow for this. If the sample size is less than 30 and the distribution is skewed, use a non-parametric test to analyze the data, as these same statistical concepts require it.
Normal or skewed distributions
Normal distributions have most data in the center with equal amounts evenly distributed to the left and right. Skewed distributions have data clumped on one side with decreasing amounts trailing off to the left or right. Skewed distributions can be right skewed or left skewed depending on which side has more data clumped together.
Single or multiple linear practice problems
The document describes two practice problems involving predicting systolic blood pressure (BPR) from patient data. The first problem involves using age alone to predict BPR, which requires single linear regression. The second problem involves using both age and gender to predict BPR, requiring multiple linear regression as it has two predictor variables.
Non parametric relationship - practice problems
The document describes several practice problems involving determining the type of relationship between different variables. The first problem involves determining the relationship between patients' age (skewed variable) and systolic blood pressure (skewed variable). The second examines the relationship between blood pressure (scaled variable) and gender (dichotomous variable). The third looks at the relationship between religious affiliation (dichotomous variable) and depression status (dichotomous variable). The fourth analyzes the relationship between age (scaled variable) and nationally ranked blood pressure (ordinal variable). Finally, the fifth considers the link between gender (dichotomous variable) and support for a proposition (dichotomous variable).
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The document reports on a multiple linear regression analysis conducted in SPSS to determine if the number of cyberbullying incidents and self-esteem scores can predict absenteeism. The analysis found that both cyberbullying incidents and self-esteem scores were statistically significant predictors of absenteeism, explaining 39.2% of the variance. The regression equation for predicting absenteeism is: Predicted Absenteeism = -.712 + .167(Cyberbullying Incidents) + .024(Self-Esteem Score).
Rank order relationship (ties) practice problems
The director of a health clinic asked to determine the relationship between patients' age and their systolic blood pressure or nationally ranked blood pressure. Kendall's Tau is used when there are ties among the ranked variables, while Spearman's Rho is used when there are no ties. In problems 1, 3, and 4 there were ties, so Kendall's Tau was the appropriate test, while in problem 2 there were no ties so Spearman's Rho was suitable.
Parametric or non parametric relationship practice problems
1. The document presents 6 practice problems involving determining whether to use parametric or non-parametric methods to analyze relationships between variables.
2. The problems involve different variable types including continuous/scaled, categorical/nominal, and ranked variables.
3. The correct method - parametric or non-parametric - depends on whether the variables are normally distributed and meet other assumptions as well as whether they are continuous or categorical.
Predictive or arbitrary relationship practice problems
The director of a health clinic asked if patients' age influences their systolic blood pressure. The data shows higher blood pressure levels generally associated with older patient ages, indicating a predictive relationship between age and blood pressure.
The director also asked if there is a relationship between systolic blood pressure and stress levels. The data presented suggests blood pressure levels tend to be higher with greater reported stress levels, indicative of a predictive relationship between stress and blood pressure.
The director provided two datasets and asked if age influences blood pressure in one case and if there is a relationship between blood pressure and stress in the other, with both situations presenting predictive relationships between the variables based on the information given.
Levels of an independent variable (Practice)
The document presents 5 practice problems involving determining the number of levels of the independent variable in different research studies. The problems include studies looking at: 1) student lunch requests and GPA with 2 levels, 2) religious beliefs and depression with 2 levels, 3) suicidal thoughts across grades with 4 levels, 4) college acceptance rates across gender with 2 levels, and 5) time management skills before and after a course with 2 levels.
Non parametric relationship (names) - practice problems
The document describes 5 practice problems involving determining the appropriate correlation coefficient to quantify relationships between variables. For problems involving a continuous variable and an ordinal or skewed continuous variable with no ties, Spearman's Rho is appropriate. When the skewed continuous variable has ties, Kendall's Tau is suitable. Point-biserial correlation is used when one variable is dichotomous and the other is continuous. Phi coefficient examines relationships between two dichotomous variables.
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Tutorial parametric tests
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Quick reminder nonparametric tests
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Is the distribution normal or skewed?
The document discusses normal and skewed distributions. It provides an example of student study hours to illustrate how to create a distribution from a data set. The distribution plots the hours of study on the x-axis and the number of occurrences on the y-axis. It then calculates the mean of the example data set to demonstrate that the mean describes the center point of a normal distribution well.
Quick reminder parametric - nonparametric difference
This document provides guidance on choosing between parametric and non-parametric tests based on the characteristics of results data. It asks whether the data is scaled and normally distributed, and if the sample sizes are greater than 30, to determine if a parametric or non-parametric test is more appropriate.
Tutorial normal v skew distributions
The document discusses normal and skewed distributions. It provides an example of student study hours to illustrate how to create a distribution from a data set. The distribution plots hours of study on the x-axis and number of occurrences on the y-axis. It then calculates the mean of the example data set to demonstrate that the mean describes the center point of a normal distribution when the majority of the data is in the middle with decreasing amounts towards the tails.
Diff rel gof-fit - jejit - practice (5)
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Quick reminder sample size
The document discusses sample size and recommends selecting the "sample sizes equal to or greater than 30" option if the sample size of both groups in a problem is equal to or greater than 30, otherwise selecting the "sample sizes less than 30" option. It notes that this recommendation provides the foundation for all inferential statistics and directs the reader to additional resources for a more thorough explanation of why sample size is important.
Quick tip sample size
If at least one sample size is greater than 30, use a parametric test even if the distribution is skewed, as probability density, normal distributions, sampling distributions, and standard error concepts allow for this. If the sample size is less than 30 and the distribution is skewed, use a non-parametric test to analyze the data, as these same statistical concepts require it.
Normal or skewed distributions
Normal distributions have most data in the center with equal amounts evenly distributed to the left and right. Skewed distributions have data clumped on one side with decreasing amounts trailing off to the left or right. Skewed distributions can be right skewed or left skewed depending on which side has more data clumped together.
Single or multiple linear practice problems
The document describes two practice problems involving predicting systolic blood pressure (BPR) from patient data. The first problem involves using age alone to predict BPR, which requires single linear regression. The second problem involves using both age and gender to predict BPR, requiring multiple linear regression as it has two predictor variables.
Non parametric relationship - practice problems
The document describes several practice problems involving determining the type of relationship between different variables. The first problem involves determining the relationship between patients' age (skewed variable) and systolic blood pressure (skewed variable). The second examines the relationship between blood pressure (scaled variable) and gender (dichotomous variable). The third looks at the relationship between religious affiliation (dichotomous variable) and depression status (dichotomous variable). The fourth analyzes the relationship between age (scaled variable) and nationally ranked blood pressure (ordinal variable). Finally, the fifth considers the link between gender (dichotomous variable) and support for a proposition (dichotomous variable).
Quick reminder is this a central tendency - spread - symmetry question(2)
This document discusses three types of questions about data distribution: central tendency, which represents the average or most common data point; spread, which is the dispersion or range of scores around the central tendency; and symmetry, which is the shape of the distribution and whether it is symmetrical or asymmetrical. Keywords for each type are provided.
17 multiple-linear regression
The document reports on a multiple linear regression analysis conducted in SPSS to determine if the number of cyberbullying incidents and self-esteem scores can predict absenteeism. The analysis found that both cyberbullying incidents and self-esteem scores were statistically significant predictors of absenteeism, explaining 39.2% of the variance. The regression equation for predicting absenteeism is: Predicted Absenteeism = -.712 + .167(Cyberbullying Incidents) + .024(Self-Esteem Score).
Rank order relationship (ties) practice problems
The director of a health clinic asked to determine the relationship between patients' age and their systolic blood pressure or nationally ranked blood pressure. Kendall's Tau is used when there are ties among the ranked variables, while Spearman's Rho is used when there are no ties. In problems 1, 3, and 4 there were ties, so Kendall's Tau was the appropriate test, while in problem 2 there were no ties so Spearman's Rho was suitable.
Parametric or non parametric relationship practice problems
1. The document presents 6 practice problems involving determining whether to use parametric or non-parametric methods to analyze relationships between variables.
2. The problems involve different variable types including continuous/scaled, categorical/nominal, and ranked variables.
3. The correct method - parametric or non-parametric - depends on whether the variables are normally distributed and meet other assumptions as well as whether they are continuous or categorical.
Predictive or arbitrary relationship practice problems
The director of a health clinic asked if patients' age influences their systolic blood pressure. The data shows higher blood pressure levels generally associated with older patient ages, indicating a predictive relationship between age and blood pressure.
The director also asked if there is a relationship between systolic blood pressure and stress levels. The data presented suggests blood pressure levels tend to be higher with greater reported stress levels, indicative of a predictive relationship between stress and blood pressure.
The director provided two datasets and asked if age influences blood pressure in one case and if there is a relationship between blood pressure and stress in the other, with both situations presenting predictive relationships between the variables based on the information given.
Levels of an independent variable (Practice)
The document presents 5 practice problems involving determining the number of levels of the independent variable in different research studies. The problems include studies looking at: 1) student lunch requests and GPA with 2 levels, 2) religious beliefs and depression with 2 levels, 3) suicidal thoughts across grades with 4 levels, 4) college acceptance rates across gender with 2 levels, and 5) time management skills before and after a course with 2 levels.
Non parametric relationship (names) - practice problems
The document describes 5 practice problems involving determining the appropriate correlation coefficient to quantify relationships between variables. For problems involving a continuous variable and an ordinal or skewed continuous variable with no ties, Spearman's Rho is appropriate. When the skewed continuous variable has ties, Kendall's Tau is suitable. Point-biserial correlation is used when one variable is dichotomous and the other is continuous. Phi coefficient examines relationships between two dichotomous variables.
Running & Reporting an One-way ANCOVA in SPSS
Administrators at Parday University asked researchers to study differences in average sleep hours among freshmen, sophomores, juniors, and seniors, controlling for gender. The researchers hypothesized there would be a significant difference in sleep hours between freshmen at the beginning and end of the semester after accounting for gender. An analysis of covariance (ANCOVA) was conducted with year in school as the independent variable, average sleep hours as the dependent variable, and gender as the covariate. The ANCOVA results showed a significant difference in average sleep hours among years in school after removing the effect of gender, but gender was not a significant covariate.
Quick reminder parametric - nonparametric relationship
This document provides guidance on choosing between parametric and non-parametric statistical tests. It first considers whether both variables are scaled and their distributions are normal; if so, a parametric test is suitable. If one variable is ordinal or nominal, or one distribution is skewed, a non-parametric test should be used. It further narrows the choice depending on whether both sample sizes are greater than 30.
Tutorial parametric tests
Parametric methods examine sample statistics as representations of population parameters when the data is scaled, the distribution is either normal or skewed, and the sample size is greater than or equal to 30 subjects. Parametric methods assume the data follows certain statistical distributions.
Quick reminder nonparametric tests
Non-parametric methods are statistical techniques used to analyze sample statistics as representations of population parameters when the distribution is skewed with less than 30 subjects, the data is ordinal or nominal, or the data consists of rankings like percentiles. Non-parametric methods do not rely on assumptions about the distribution of variables and are appropriate for small sample sizes or non-numerical data types.
What is a one sample wilcoxon test
The document explains the one-sample Wilcoxon test, which is used to determine if a sample is statistically similar to or different from a population. It provides an example comparing IQ scores, where researchers took a random sample of 20 people and want to know if the sample's mean of 70 is representative of the overall population's mean of 100. The one-sample Wilcoxon test compares the sample and population medians to determine the probability that the sample and population are statistically similar or different.
Is the distribution normal or skewed?
The document discusses normal and skewed distributions. It provides an example of student study hours to illustrate how to create a distribution from a data set. The distribution plots the hours of study on the x-axis and the number of occurrences on the y-axis. It then calculates the mean of the example data set to demonstrate that the mean describes the center point of a normal distribution well.
Quick reminder parametric - nonparametric difference
This document provides guidance on choosing between parametric and non-parametric tests based on the characteristics of results data. It asks whether the data is scaled and normally distributed, and if the sample sizes are greater than 30, to determine if a parametric or non-parametric test is more appropriate.
Tutorial normal v skew distributions
The document discusses normal and skewed distributions. It provides an example of student study hours to illustrate how to create a distribution from a data set. The distribution plots hours of study on the x-axis and number of occurrences on the y-axis. It then calculates the mean of the example data set to demonstrate that the mean describes the center point of a normal distribution when the majority of the data is in the middle with decreasing amounts towards the tails.
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Quick reminder is this a central tendency - spread - symmetry question(2)
Quick reminder is this a central tendency - spread - symmetry question(2)
Parametric or non parametric relationship practice problems
Parametric or non parametric relationship practice problems
Predictive or arbitrary relationship practice problems
Predictive or arbitrary relationship practice problems
Non parametric relationship (names) - practice problems
Non parametric relationship (names) - practice problems
Quick reminder parametric - nonparametric relationship
Quick reminder parametric - nonparametric relationship
Quick reminder parametric - nonparametric difference
Quick reminder parametric - nonparametric difference
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The document provides explanations and examples for different types of statistical questions:
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Normal or skewed distributions (inferential) - Copyright updated
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The document discusses different types of data:
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- Nominal data names or categorizes values like Republican or Democrat.
- Nominal proportional data are simply percentages like Republican 45% or Democrat 55%.
Nature of the data (spread) - Copyright updated
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The document discusses two types of data: scaled data and ordinal data. Scaled data can be measured in exact amounts with equal intervals between values. Ordinal or ranked data provides comparative amounts but not necessarily equal intervals. Several examples are provided to illustrate the difference, including driving speed, states ranked by well-being, and elephant weights. Practice questions are also included for the reader to determine if data examples provided are scaled or ordinal.
Dichotomous or scaled
The document discusses whether variables are dichotomous or scaled when calculating correlations. It provides examples of correlations between ACT scores and whether students attended private or public school. One example has ACT scores as a scaled variable and school type as dichotomous. Another has lower and higher ACT scores as dichotomous and school type as dichotomous. It emphasizes determining if variables are both dichotomous, or if one is dichotomous and one is scaled.
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The document discusses the correlation between ACT scores and a measure of school belongingness. It determines that one of the variables, which has a sample size less than 30, is skewed and has many ties. As a result, a non-parametric test should be used to analyze the relationship between the two variables.
Skewed sample size less than 30
The document discusses using parametric versus non-parametric tests based on sample size for skewed distributions. For skewed distributions with a sample size less than 30, a non-parametric test is recommended. For skewed distributions with a sample size greater than or equal to 30, a parametric test is recommended. It provides examples analyzing the correlation between ACT scores and sense of school belongingness using both approaches.
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The document discusses whether there are many ties or few/no ties within the variables of the relationship question "What is the correlation between ACT rankings (ordinal) and sense of school belongingness (scaled 1-10)?". It determines that ACT rankings, being ordinal, have many ties, while sense of school belongingness, being on a scale of 1-10, may have many or few ties depending on how scores are distributed.
Ordinal and nominal
The document discusses identifying whether variables in statistical analyses are ordinal or nominal. It provides examples of relationships between variables such as ACT rankings and sense of school belongingness, daily social media use and sense of well-being, and private/public school enrollment and sense of well-being. It asks the reader to identify if variables in examples like running speed and shoe/foot size or LSAT scores and test anxiety are ordinal or nominal.
Relationship covariates
The document discusses covariates and their impact on relationships between variables. It defines a covariate as a variable that is controlled for or eliminated from a study. It explains that if a covariate is related to one of the variables in the relationship being examined, it can impact the strength of that relationship. Examples are provided to demonstrate when a question involves a covariate or not.
Relationship nature of data
This document discusses the nature of variables in relationship questions. It can be determined that the variables are either both scaled, at least one is ordinal, or at least one is nominal. Examples of different relationship questions are provided that fall into each of these categories. The document also provides practice questions for the user to determine which category the variables fall into.
Number of variables (predictive)
The document discusses the number of variables involved in research questions. It explains that many relationship questions deal with two variables, such as gender predicting driving speed. However, some questions deal with three or more variables, for example gender and age predicting driving speed. The document asks the reader to identify whether example research questions involve two or three or more variables.
Levels of the iv
The document discusses independent and dependent variables in research questions. It provides examples to illustrate that an independent variable has at least two levels and may have more, such as religious affiliation having two levels (Western religion and Eastern religion) or company type having three levels (Company X, Company Y, Company Z). It then provides a practice example about employee satisfaction rates among morning, afternoon, and evening shifts, identifying shift status as the independent variable with three levels.
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The document discusses independent variables and how they relate to research questions. It provides examples of questions with one independent variable, two independent variables, and zero independent variables. An independent variable influences or impacts a dependent variable. Questions are presented about employee satisfaction rates, agent commissions, training proficiency, and cyberbullying incidents to illustrate different numbers of independent variables.
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at Integral University, Lucknow, 06.06.2024
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Thinking of getting a dog? Be aware that breeds like Pit Bulls, Rottweilers, and German Shepherds can be loyal and dangerous. Proper training and socialization are crucial to preventing aggressive behaviors. Ensure safety by understanding their needs and always supervising interactions. Stay safe, and enjoy your furry friends!
ISO/IEC 27001, ISO/IEC 42001, and GDPR: Best Practices for Implementation and...
Denis is a dynamic and results-driven Chief Information Officer (CIO) with a distinguished career spanning information systems analysis and technical project management. With a proven track record of spearheading the design and delivery of cutting-edge Information Management solutions, he has consistently elevated business operations, streamlined reporting functions, and maximized process efficiency.
Certified as an ISO/IEC 27001: Information Security Management Systems (ISMS) Lead Implementer, Data Protection Officer, and Cyber Risks Analyst, Denis brings a heightened focus on data security, privacy, and cyber resilience to every endeavor.
His expertise extends across a diverse spectrum of reporting, database, and web development applications, underpinned by an exceptional grasp of data storage and virtualization technologies. His proficiency in application testing, database administration, and data cleansing ensures seamless execution of complex projects.
What sets Denis apart is his comprehensive understanding of Business and Systems Analysis technologies, honed through involvement in all phases of the Software Development Lifecycle (SDLC). From meticulous requirements gathering to precise analysis, innovative design, rigorous development, thorough testing, and successful implementation, he has consistently delivered exceptional results.
Throughout his career, he has taken on multifaceted roles, from leading technical project management teams to owning solutions that drive operational excellence. His conscientious and proactive approach is unwavering, whether he is working independently or collaboratively within a team. His ability to connect with colleagues on a personal level underscores his commitment to fostering a harmonious and productive workplace environment.
Date: May 29, 2024
Tags: Information Security, ISO/IEC 27001, ISO/IEC 42001, Artificial Intelligence, GDPR
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The History of Stoke Newington Street Names
Presented at the Stoke Newington Literary Festival on 9th June 2024
www.StokeNewingtonHistory.com
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This will be used as part of your Personal Professional Portfolio once graded.
Objective:
Prepare a presentation or a paper using research, basic comparative analysis, data organization and application of economic information. You will make an informed assessment of an economic climate outside of the United States to accomplish an entertainment industry objective.
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Physiology and chemistry of skin and pigmentation, hairs, scalp, lips and nail, Cleansing cream, Lotions, Face powders, Face packs, Lipsticks, Bath products, soaps and baby product,
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