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Forecasting Powerpoint Template

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Medical PowerPoint Template

Transcript: Medical PowerPoint Template Design Elements Color Schemes for Medical Presentations Font Selection for Readability Color schemes significantly affect audience understanding and retention. In medical presentations, using blue and green hues promotes calmness and trust, while contrasting colors can highlight key information and enhance visibility. Choosing the right font is crucial for comprehension. Sans-serif fonts like Arial or Helvetica are recommended as they are easier to read on screens. Always ensure that text is large enough to be legible from a distance. Incorporating Graphics and Images Layout and Structure Incorporating relevant graphics can enhance understanding and retention of complex ideas. Use high-quality images, charts, or diagrams that directly relate to the content to support the narrative without overcrowding the slide. A well-structured layout guides the audience’s eye and improves information flow. Utilize a grid system to maintain alignment and consistency, making sure to reserve space for visual elements. Balance text with images to avoid clutter. A Blank Canvas for Your Data Presentation Tips Best Practices for Delivery Content Organization in Medical Presentations Engaging Your Audience Practicing your presentation can lead to smoother delivery and reduced anxiety. Utilize appropriate body language, voice modulation, and eye contact to foster a connection with the audience, making your message more impactful. Audience engagement is critical for effective communication. Techniques include asking rhetorical questions, using relatable examples, and incorporating multimedia elements to maintain interest and encourage participation. Title Slides and Headings Introduction to Medical Presentations Title slides set the stage for your presentation and should include the topic, your name, and the date. Headings throughout the presentation guide the audience through the narrative and facilitate smooth transitions between topics, ensuring clarity and focus on key messages. Bullet Points vs. Paragraphs Handling Questions and Feedback Bullet points provide concise and digestible pieces of information, making it easier for the audience to follow along. In contrast, paragraphs may be necessary for complex concepts but should be used sparingly to maintain attention and avoid overwhelming the viewer. Practicing and Timing Your Presentation Using Tables and Charts Tables and charts effectively present quantitative data, making complex information more approachable. They facilitate quick understanding of trends and relationships within data, enhancing the audience’s ability to interpret clinical findings or statistical results. Rehearse your presentation multiple times to refine your delivery and timing. Understanding how long each section takes helps ensure that you cover all material without rushing or exceeding your allotted time. Encourage questions to create a dialogue with your audience. Responding thoughtfully to feedback shows respect for their input and enhances clarity for everyone involved, improving overall comprehension. Citing Sources and References Importance of Visual Aids Citing sources is crucial in maintaining credibility and allowing the audience to explore further. Proper referencing not only attributes the original work but also strengthens arguments presented in the medical content, supporting evidence-based practice. Visual aids play a crucial role in medical presentations by simplifying complex information. They help audiences grasp essential concepts quickly, improving retention and engagement through the use of charts, images, and videos. Overview of PowerPoint Features PowerPoint offers various features to enhance medical presentations, including templates specifically designed for medical content, the ability to incorporate multimedia, and options for animations that can illustrate processes or changes over time. Objectives of the Medical Template The medical PowerPoint template serves to streamline the creation of presentations by providing a standardized format. This ensures consistency in design and aids users in organizing their data effectively for clarity and impact.

FORECASTING

Transcript: 3.3 The Strategic Importance of Forecesting Good Forecasts are of critical importance in all aspect of a business. The forecast is the only estimate of demand. 2. DELPHI METHOD Associative Models- variables or factors that might influence the quantity being forecast. 3.5.2. OVERVIEW OF QUANTITATIVE METHOD 1. JURY OR EXECUTIVE 3.6.4 Exponential Smoothing -sophisticated weighed moving average forecasting method that involves very little record keeping of past data New forecast = Last period’s forecast + a (last period’s actual demand – last periods forecast) Time Series – predict on the assumption that the future is a function of the past. V. SUMMARY Qualitative approaches employ judgment, experience intuition and a host of other factors that are difficult to quantity, quantitative forecasting use historical data and casual or associative, relations to project future demand. I. INTRODUCTION II. OBJECTIVES III. MAIN CONTENT III.1 What is Forecasting III.1.1 Forecasting Time Horizons III.1.2 The Influence of Product Life Cycle III.2 TYPES OF FORECASTS III.3 THE STRATEGIC INFLUENCE OF FORECASTING III.3.1 Human Resources III.3.2 Capacity III.3.3 Supply Chain Management III.4 SEVEN STEPS IN THE FORECASTING SYSTEM III.5 FORECASTING APPROACH III.5.1 Overview of Qualitative Method III.5.2 Overview of Quantitative Method III.6 TIME SERIES FORECASTING III.6.1 Decomposition of a Time Series III.6.2 Naïve Approach III.6.3 Moving Averages III.6.4 Exponential Smoothing III.6.5 Measure Forecast Error III.7 ASSOCIATIVE FORECASTING METHODS: Regression and Correlation Analysis III.7.1 Using Regression Analysis to Forecast IV. Conclusion V. Summary 3.6.5 MEASURING FORECAST ERROR Forecast Error = Actual Demand – Forecast Value = AT-FV Five Methods: Two Categories: 1. Naïve Approach Time Series 2. Moving Average Time Series 3. Exponential Smoothing Associative Model 4. Trend Projection Associative Model 5. Linear Regression Associative Model III. What is Forecasting? high level experts/managers, combined with statistical models are pooled to arrive at a group of demand 3.6.1 Decomposition of Time Series -analyzing time series means breaking down data into components and then projecting them forward. 3.3.1 Capacity When it is inadequate, the resulting shortages can mean undependable delivery, loss of customer and loss of market share. This is exactly what happens to Nabisco when it underestimated the huge demand for its new low fat Snackwell Devils Food Cookies. Even with production lines working overtime, Nabisco could not keep up with demand, and it lost customers. When excess capacity is built, on the other hand, costs can skyrocket. 3.5 FORECASTING APPROACH FORECASTING 3.3.1 SUPPLY CHAIN MANAGEMENT Good supplier relations and the ensuing price advantage for materials and parts depend on accurate forecasts. IV. CONCLUSION Forecasts are a critical part of the operations managers’ decision. It drives a firm’s production, capacity and scheduling systems and affects the finances, marketing and personal planning. II. OBJECTIVES Products and even services do not sell at a constant level throughout their lives. Most successful products pass through 4 stages: 3.1.1. Forecasting Time Horizons 3 Categories 1. Short Range Forecast – Time span up to 1 year but is generally less than 3 months. Used for: • Planning • Purchasing • Job Scheduling • Work force levels • Job assignments • Production levels 2. Medium Range Forecast (Intermediate Forecast, - 3months to 3 years – It is useful in: • Sales Planning • Production Planning and Budgeting • Cash and Budgeting • Analyzing Various Operating Plans 3. Long Range Forecast (Generally 3 years or more in time span Used in : • Planning for new products • Capital Expenditures • Facility Location/Expansion • Research & Development Describe or explain • Moving average • Exponential smoothing • Seasonality TOPICAL OUTLINE 3.6 TIME SERIES FORECASTING I. INTRODUCTION Moving Averages -uses a number of historical data values to generate a forecast. Moving Average = Demands in previous n periods / N Where N is the number of periods in the moving average for ex. 4,5, or 6 months respectively for a 4, 5, or 6 period moving average. "It is no use saying we are doing our best. You have got to succeed in doing what is necessary..." - Winston Churchill solicits inputs from customers or potential customers regarding future purchasing plans) based on informal conversations with customers 1. Determine the use of the forecast 2. Select the items to be forecasted 3. Determine the time horizon of the forecasts 4. Select the forecasting model 5. Gather the data needed to make the forecast 6. Make the forecast 7. Validate and implement the results. 3.5.1 OVERVIEW OF QUALITATIVE METHOD QUANTITATIVE – uses variety of mathematical models that rely on historical data and (or casual) variables to forecast demand. Naïve Approach -assumes that demand in the next period will be equal to demand in the most recent period. 3.7

Forecasting

Transcript: Climate~ the weather conditions prevailing in an area in general or over a long period. Weather Stratus Clouds~ a large dark low cloud Forecast~ predict or estimate Precipitation~ rain, snow, sleet, or hail that falls to the ground Humidity~ the state or quality of being humid Wind ~ natural movement of the air in the form of a current of air blowing from a particular direction. Forecasting Warm Front~ the boundary of an advancing mass of warm air, in particular the leading edge of the warm sector of a low-pressure system. Weather~ the climate at any given time Name Cold Front~ the boundary of an advancing mass of cold air, in particular the trailing edge of the warm sector of a low-pressure system

Forecasting

Transcript: Cold Front Stationary Front Occluded Front Station Model Climate A line showing weather patterns drawn or shown on a weather map, used to indicate the creation or movement of a weather system. A certain pattern for entering weather symbols, Isobar Forecasting By Julianah Perialas A front (two air masses meeting) bringing in a cold air mass. The boundary between two air masses that meet, but don't remove each other. When warm air is forced above cold air, and they meet, the cold air forces the warm air away from the Earth's surface. Step 4

Forecasting Template

Transcript: Forecasting Summary Each Tab Final Notes: Do not delete cells where there are formulas Do not delete entire rows Do not do cutting and pasting Note: It is always better to manually delete and retype where a change is needed so as to make sure no formulas get erased Ask us questions, we're here to help! Meet with us at the end to review forecast Keep an eye on the system Pay Forecast Non-Pay

NATHO POWERPOINT TEMPLATE

Transcript: Wrap up quickly * Summarize each point you made * Give quick call-backs so your audience remembers * Make it clear this is the end The results Dive deeper Put a bold statement here This is where your presentation starts. Provide context for your audience and make it easy for them to follow. Provide statistics, go into detail, or more — whatever works best for your presentation. Keep your words short and punchy so your audience stays focused. You can add imagery and frames to keep their attention where you want it. Provide any important context here Put a bold statement here 1 Short headline goes here Keep your words short and punchy so your audience stays focused. You can add imagery and frames to keep their attention where you want it. Provide any important context here. Introduce a topic 2 Name your presentation * Quickly cover the important parts of this topic * Limit your words so your audience stays focused * Use visuals to help you 3 6 You can put a subtitle here Quote or caption here 4 5 Introduce a topic Make a bold statement * Quickly cover the important parts of this topic * Limit your words so your audience stays focused * Use visuals to help you Use this space for details that you haven’t talked about yet. Relate what you’re saying to the nearby image so your audience has a visual, too.

PowerPoint Portrayal Template

Transcript: huddle Mrs. Rossi huddle to crowd together crowd huddle separate

Forecasting

Transcript: Location: American River Basin Forecast points: 8 (referred to the 8 watersheds supplying the 8 powerhouses) Forecasted variable: runoff from the prediction month to July Available data: historical runoffs by month precipitations data (8 stations) snowpack measurements (19 snow courses) Assumptions The model depends on the month (what are the most predictible variables?) Ex: February Aim: For each point predict the monthly inflow from February to July Precipitation: 2 variables (Jan and Nov-Dec) use of indices and averages... Oct-Jan runoff (at the considered point Snowpack: 19 snow courses 2 indices (based on PCA), 1 used Historical data: October 1975 to September 2007 Principal component analysis Water Supply Forecasting Historical water year analysis Overview Predictor variables Monthly forecast Computation: for each forecast point... Linear regression using the first component Gives the Feb-July runoff 1. Historical water year inflow fitted to a Gamma distribution 2. Exceedance proba (EP) determined via the distribution For each EP: a. Apr-Jul and Oct-Mar runoff found using other fitted Gamma dist. b. Aug-Sept inflow computed by regression using Apr-Jul c. The 3 inflow are combined to adjust the water year inflow Disaggregation snowmelt season and summer: d. Apr, May, June, July inflows from Apr-Jul (4 different regressions) e. August abd Sept from Aug-Sept (2 regressions) Disaggregation of the snow accumulation season: f. March using fit to Gamma distribution g. Oct-Feb by substracting March to Oct-March h. Oct, Nov, Dec, Jan, Feb fronm Oct-Feb inflow (5 regressions) Aim of this analysis: to have an idea of the annual and monthly inflows (with exceedance probabilities) without any information about the current year. Offsets from median values normally distributed errors homoscedastic distribution of the residuals independence of the observations variates normally distributed linear relationship between independent and dependent variates Water year = from October to September

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