Office excel 2016

Data were entered into Microsoft Office Excel 2016 and exported into the statistical Package for Social Sciences (SPSS) software version 26 for analysis. Continuous variables, including age, gestational age, parity, gravidity, interpregnancy period, number of antenatal care visits, gestational age at booking visit, number of ultrasounds, number of IPTs, gestational age of rupture of membranes, and birth weight, were categorized and used as such for analysis. Missing data were not included in the final analysis. The stillbirth rate was calculated as the number of stillbirths per 1000 live births. The distribution of exposure variables amongst the cases and controls was described using frequency tables. Associations between exposure variables and stillbirth were assessed using the chi-square test or Fisher’s exact test in cases where at least one expected frequency was less than 5. Variables with p values less than 0.1 were subsequently included in a multivariable logistic regression to determine adjusted odds ratios while controlling for confounding factors. Statistical significance was set at p < 0.05. Adjusted odds ratios and 95% confidence intervals were reported.

All data were analyzed using Microsoft Office Excel 2016 and Statistical Package for the Social Sciences (SPSS) (SPSS. v.20, Chicago, IL, USA). Farm seroprevalence was determined by the ratio of positive farms to the total number of farms visited. A positive farm contained at least one seropositive cow. Cow seroprevalence was measured by the number of seropositive cows to the total number of cows examined. Confidence interval (CI) was constructed at the 95% level of confidence.
CI was calculated using the following formula:
CI=P±Pa.
P is seroprevalence obtained. Pa is absolute precision. Pa has been calculated using the following formula:
The relationship between seroprevalence and neosporosis risk factors was determined using univariable and multivariable logistic regression models [17 ]. The first phase was a univariable analysis of variables by a Chi-square test and crude odds ratios (OR). In the multivariable analysis, significant variables at p≤0.2 were chosen. The overall fit of the logistic regression models was tested with the Hosmer-Lemeshow test. When the OR was greater than 1 and p≤0.05, variables were considered risk factors. Multifactorial correspondence analysis (MCA) was also performed and is a graphical presentation applicable to categorical data tables in addition to logistic regression [18 ].

Data processing was conducted using Microsoft Office Excel 2016, and all statistical analyses was conducted using the SPSS 21.0 software package. One-way analysis of variance and the least significant difference test (LSD) were used to check the differences of plum yield and environmental impacts per unit among the different groups.

We used Microsoft Office Excel 2016 and SPSS 24.0 for data analysis. Descriptive data were presented as the number, percentage, mean, and standard deviation, wherever appropriate. We compare the study groups via student t-test and Mann-Whitney U test for normally and non-normally distributed continuous variables, respectively; and chi-square test for categorical variables. An overall 5% of Type-I error level was used to infer statistical significance.

Microsoft Office Excel (2016) was used for data recording, and SPSS for Windows (version 26) was used for data analysis. Descriptive statistics were performed. Categorical variables are summarized by percentages, and continuous data are summarized by the median and interquartile range (IQR) after checking for normality.