Linear A

Supply chains that are complex are commonly modeled as linear programs (LPs). They can effectively trade off broad range of criteria. To model FMCG supply chains accurately, one must include discrete aspects of decision making, which requires solving a mixed-integer program (MIP). It has become significantly important for managers, given the widespread use of linear models today, to be able to develop good, efficient models to aid them in the decision-making process. Three important factors;…

ingredients, including chili beans and ground beef. The number of 100-gallon batches of Razorback chili (x1) and Longhorn chili (x2) that can be produced daily is constrained by the availability of chili beans and ground beef, as shown in the following linear programming model: maximize Z = 200x1 + 300x2 (profit, $) subject to 10x1 + 50x2 … 500 (chili beans, lb.) 34x1 + 20x2 … 800 (ground beef, lb.) x1, x2 Ú 0 The final optimal simplex tableau for this model is as follows: cj 300 200 Basic…

In this paper, I will be discussing two different theories to explain the crime of robbery. The two theories I will be focusing on will be the rational choice theory and the routine activity theory. In 2011, the Federal Bureau of Investigations (F.B.I.) Uniform Crime Reporting Program (U.C.R.) estimates that there were approximately 354,396 robberies in the US accounting for $409 million in losses of individuals (Federal Bureau of Investigation, 2011). The F.B.I.’s U.C.R. defines robbery as: The…

Knossos is located in Crete, on the largest island off the coast of Greece, 5 kilometers southeast of the city of Heraklion (1). Being nestled within a valley near the river of Kairatos, the river currently flows only partly year-round (1). Yet, during Minoan times when the site was inhabited, the river flowed constantly. Knossos used to be surrounded with cypress and oak trees but has since been turned into acres of vineyards and olive trees (1). Knossos was established during the Bronze Age…

There were two single linear regressions made for the data set. A new independent variable was made for each. The independent variable for the first single linear regression was recoded as associate and below. This particular variable included the data for the educational levels of an associate degree or lower education. In the second single linear regression the independent variable was labeled bachelor and above. This new variable included the data for the educational levels of a bachelor…

relationship between the variables of interest. This can be described in numerous ways; this also depends on the analysis. This is concerned with how each variable is related to other similar variables. This association is based on the strength of the linear relationship in the degree of monotonicity. To the degree that it is based on counting various pairs in a relationship. Known as a statistical tool that investigates relationships among variables, regression analysis seeks to determine the…

intervention is small and not clinically significant. Association and causality is not proven in this case. The data set given was imported to Stata and summary statistics were used and graphs and scatter plots on Stata to visualise and analyse the data. 3 A linear regression analysis was done with Stata to find the treatment effect to adjust for baseline co variates. (propensity score matching) The results are as below- weight after treatment being the dependent variable (continuous…

Moreover, assumption of a linear relationship between the independent and dependent variables is necessary because standard multiple regression can only accurately estimate the linear relationship. The last assumption need to be satisfied is the assumption of homoscedasticity. A residual analysis and a fitness test are conducted to verify the assumption before the analysis…

From there I can add a regression line, find the equation of my model and the correlation coefficient. This will indicate how strong a relationship there is between my two variables. I can also use my data to plot a residual plot to prove that a linear model is a good fit for my data.…

an increase from the projected sales by $8000 dollars. This increase would equate to a 2.7% change from the forecast and within the 3.6% standard error percentage. The data in this forecast model is built the correlation between the seasonal and linear yearly growth of sales for C’mon Back Restaurant. This model assumes no other independent variable which could impact sales. As Puerto Rico is a Caribbean nation, it is subject to severe weather patterns, hurricanes. The impact of a hurricane…